This paper presents a Physical Memory Protection Snapshot Engine (PSE), a lightweight hardware extension for RISC-V that addresses both performance and security challenges of Physical Memory Protection (PMP) reconfiguration. By storing and restoring full PMP configurations in a single cycle, the PSE drastically reduces the overhead of context switches typically used in Trusted Execution Environments (TEEs) and secure real-time systems. At the same time, the redundant storage and two-dimensional parity protection provide an efficient and effective defense against fault injection attacks that target PMP registers. In 100k randomized trials, our experimental results demonstrate that the PSE can reliably detect and prevent FI-induced privilege escalations, while incurring only 11.7% area overhead. This makes it a practical solution for embedded devices where both efficiency and trustworthiness are essential.

Binary Neural Networks (BNNs) have obtained a strong foothold in the field of machine learning at the edge due to their minimal hardware requirements. However, their energy and performance efficiency remain hindered by frequent data transfer between memory and processors. Computation-in-memory (CIM) architectures address this problem by embedding processing units within the memory. Unfortunately, current implementations of CIM are susceptible to IP piracy attacks through side channels. This paper presents a novel secure periphery scheme for NN accelerators with sequential accumulation that conceals IP information by obscuring the power consumption of the counter responsible for the leakage. This is achieved by combining two innovative techniques: operand schedule randomization and an always-count Gray code counter. The results demonstrate that the proposed design effectively resists power side channel attacks (SCAs). Moreover, Signal-to-Noise Ratio (SNR) and Test Vector Leakage Assessment (TVLA) show safe leakage levels. Compared to the state-of-the-art, our countermeasure reduces area and power overheads by up to 12.7× and 13.3×, achieving only 37% area and 51.2% power overhead with the added protection logic. Notably, this enhanced security comes with zero latency overhead, maintaining the performance of the baseline design.

The development of Ferroelectric Field-Effect Transistor (FeFET) manufacturing requires high-quality test solutions, yet research on FeFET testing is still in a nascent stage. To generate a dedicated test method for FeFETs, it is critical to have a deep understanding of manufacturing defects and accurately model them. In this work, we introduce the unique defect, Anomalous Charge Trapping (ACT), in FeFETs. The ACT-defective FeFET is characterized, and the physical mechanism of the defect is explained. Then, we apply the Deviceaware Test (DAT) method to design a specific ACT-defective FeFET model, which includes the physical impact of the defect on the electrical parameters of defect-free models, and calibrate the model with measurement data. Fault modeling is performed based on circuit-level simulations, and dedicated test solutions are proposed.

Structural testing has been very successful in the VLSI manufacturing process to screen out faulty devices and provide high outgoing product quality. However, recent reported data show that existing solutions are not good enough for advanced technology nodes and emerging device technologies. This paper discusses a new manufacturing test approach called DeviceAware Test (DAT), applies it to different flavors of emerging devices, and its potential to be used beyond just manufacturing test.

Artificial Intelligence (AI) has had a profound impact on our contemporary society, and it is indisputable that it will continue to play a significant role in the future. To further enhance AI experience and performance, a transition from large-scale server applications towards AI-powered edge devices is inevitable. In fact, current projections indicate that the market for Smart Edge Processors (SEPs) will grow beyond 70 Billion USD by 2026 [1]. Such a shift comes with major challenges, as these devices have limited computing and energy resources yet need to be highly performant. Additionally, security mechanisms need to be implemented to protect against diverse attack vectors as attackers now have physical access to the device. Besides cryptographic keys, Intellectual Property (IP), including neural network weights, may also be potential targets. The CONVOLVE [2] project (currently in its intermediate stage) follows a holistic approach to address these challenges and establish the EU in a leading position in embedded, ultra-low-power and secure processors for edge computing. It encompasses novel hardware technologies, end-to-end integrated workflows, and a security-by-design approach. This paper highlights the security aspects of future edge-AI processors by illustrating challenges encountered in CONVOLVE, the solutions we pursue including some early results, and directions for future research.

The IEEE European Test Symposium (ETS) has been facilitating progress in electronic systems testing since its launch in 1996. On the occasion of its 30th anniversary, this collaborative paper gathers sections by 21 ETS teams to outline their influential ideas and milestones. Each team’s section highlights historical perspective, current research, frameworks and projects as well as forward-looking research agendas in the area of electronic-based circuits and systems testing, reliability, safety, security and validation. This anniversary summary documents how research of various ETS teams, exemplifying the test community, has been evolving and transitioning from concepts to practical standards and Electronic Design Automation (EDA) tools and flows. This legacy is a strong base to drive the next generation of advances in electronic systems testing.

Addressing non-idealities in Resistive Random Access Memories (RRAMs) is crucial for their successful commercialization. For example, the inherent resistance drift that occurs during consecutive read operations can induce Read Disturb Faults (RDF), leading to functional errors. This paper analyzes and characterizes the resistance drift and the RDF based on data measurements and presents a physics-based RRAM compact model that incorporates these non-idealities. Additionally, an in-field mitigation scheme is proposed, leveraging bidirectional read operations to balance the resistance. The scheme is implemented and validated through circuit simulations, both for RRAM used as memory and for RRAM-based computation-in-memory microarchitectures for deep neural networks. The results demonstrate that RRAM without any mitigation scheme can start failing after 8,000 consecutive reads, while our mitigation scheme ensures that the memory remains functional even after 106consecutive reads. Furthermore, the results indicate that using the MNIST dataset as a case study, the accuracy can drop significantly from 86% to as low as 12.5% without any mitigation scheme. In contrast, the proposed mitigation scheme improves this accuracy up to 84.2%.

In this paper, we introduce a novel passive physical anti-tampering Physical Unclonable Function (PUF) based on glitters that can protect an entire Integrated Circuit (IC) and/or Printable Circuit Board (PCB). A prototype of the proposed glitter based PUF has been developed. The glitters are dropped randomly in a resin layer during its formation and their positioning is used as the basis of a PUF. The PUF response is created by taking a picture inside the coating layer. To get a stable response resilient against noise and different temperature cycles, the picture is processed using filtering, image processing, and error correction. Using actual drill measurements, our findings indicate that even drilling with a 0.1mm diameter drill can be detected and lead to a wrong PUF response.

Compute-in-memory (CIM) AI accelerators using non-volatile memories like RRAM enable energy-efficient edge inference by executing Multiply-Accumulate (MAC) operations directly in memory in a single cycle. These designs modify memory cells and analog-to-digital converters (ADCs), introducing faults not seen in standard memories. We present the first structural testing methodology and framework for RRAM-based CIM MAC circuits, including defect and fault models for memory cells and ADCs. Our robust inference-driven tests exercise full MAC functionality, significantly reducing test time compared to traditional methods, and integrating cell and peripheral testing to ensure high reliability, defect coverage, and operational efficiency.

Ferroelectric Field-Effect Transistors (FeFETs) are promising candidates for non-volatile memory (NVM) technologies, especially in embedded systems and edge computing. However, due to their physical characteristics, FeFETs exhibit unique defects—such as Threshold Voltage Shifting (TVS) caused by trap charges in the oxide layer—that are not captured by conventional defect models. This study adopts the Device-Aware Test (DAT) methodology to model these defects by incorporating their impact into the electrical parameters, calibrated using measurement data. Defect injection, circuit-level simulations, and fault analysis are performed to derive realistic fault models. Finally, the March algorithm and Design-for-Test (DfT) techniques are proposed to effectively detect these defects.

Resistive Random Access Memories (RRAMs) are now undergoing commercialization, with substantial investment from many semiconductor companies. However, due to the immature manufacturing process, RRAMs are prone to exhibit unique defects, which should be efficiently identified for high-volume production. Hence, obtaining diagnostic solutions for RRAMs is necessary to facilitate yield learning, and improve RRAM quality. Recently, the Device-Aware Test (DAT) approach has been proposed as an effective method to detect unique defects in RRAMs. However, the DAT focuses more on developing defect models to aid production testing but does not focus on the distinctive features of defects to diagnose different defects. This paper proposes a Device-Aware Diagnosis method; it is based on the DAT approach, which is extended for diagnosis. The method aims to efficiently distinguish unique defects and conventional defects based on their features. To achieve this, we first define distinctive features of each defect based on physical analysis and characterizations. Then, we develop efficient diagnosis algorithms to extract electrical features and fault signatures for them. The simulation results show the effectiveness of the developed method to reliably diagnose all targeted defects.

The Internet of Things is promising as it drives the datafication of our everyday life and thus, leverages synergies between originally considered “dead” things and enables them to proactively serve humans. IoT5.0, an Artificial Intelligence assisted Internet of Things, could even more benefit society, as the devices could even learn how to provide more value. But the ubiquitous connectivity comes at a cost. Security levels have to rise tremendously to ensure a network stays secure and safe for humans. This additional effort often is a burden for small and medium sized enterprises as the complexity and security demands of such systems rise faster than available resources. Consequently., RESILIENT TRUST focuses on end-to-end security of IoT processing chains with a focus on strong exploitation for SMEs. Moreover, RESILIENT TRUST will address and significantly mitigate the major risks to enable IoT 5.0. That way., this project will be a driver for sustainable development and the generation of convenience and wealth. A solution is proposed to ensure end-to-end security by boosting RESILIENCE and TRUST along different key supply chains of IoT device.

Modern DRAMs are vulnerable to Rowhammer attacks, demanding robust protection methods to mitigate these attacks. Existing solutions aim at increased resilience by improving design and/or adjusting operation parameters, limit row access count by throttling and prevent bit flips by timely row refreshing. However, scaling these methods for future DRAM technologies may incur significant costs in terms of area, power and/or latency. This study analyses the impact of the values of the neighbouring cells on victim cells and introduces a row alternation protection method, which is a novel approach that alternates the data of attacker rows on each access to lower the chance of bit flips in victim rows. Our analysis show that the minimum Rowhammer count to cause a bitflip in a particular cell does not only depend on vertical neighbours from the attacker row, but also on the value of the horizontal neighbours from the victim row as well as diagonal cells from the attacker row. Row alternation is able to protect the majority of the vulnerable cells (i.e., with 65%) for the DRAM used in our case study and in cases where unsuccessful it significantly increases the average minimum required Rowhammer account by 18%.

Computation-in-Memory (CIM) architectures present a promising solution for efficient implementation of Neural Networks. Particularly, SRAM-based digital CIM architectures are optimal candidates to realize them. Recent studies have revealed potential weaknesses in these architectures, particularly against power attacks. This study introduces a novel attack method enabling weight extraction through the analysis of the adder tree component within the architecture. In our attack, the k-means clustering technique is employed to identify the hamming weights of the CIM weights. Subsequently, we correlate traces belonging to known weights with traces belonging to Hamming groups with unknown weights in order to identify their weight values. As a case study, the attack was applied on SRAM CIM implementation based on 40nm TSMC technology. The results indicate that the weights stored in the CIM crossbar can be retrieved with 100% accuracy purely by analyzing the power consumption.

Due to the immature manufacturing process, Resistive Random Access Memories (RRAMs) are prone to exhibit new failure mechanisms and faults, which should be efficiently detected for high-volume production. Those unique faults are hard to detect but require specific Design-for-Test (DfT) circuit design. This paper proposes a DfT based on a parallel-reference write circuit that can detect all RRAM array faults during diagnosis, production testing, and its application in the field.

Guaranteeing high-quality test solutions for Spin-Transfer Torque Magnetic RAM (STT-MRAM) is a must to speed up its high-volume production. A high test quality requires maximizing the fault coverage. Detecting permanent faults is relatively simple compared to intermittent faults; the latter are faults (caused by non-environmental conditions) that appear and disappear as a function of time, and are therefore hard to detect. Testing for such faults in STT-MRAMs is even worse considering the Magnetic Tunneling Junction inherent property ‘intrinsic switching stochasticity’, which results in inevitable random write errors. This paper presents a novel Design-for-Testability (DFT) scheme for detecting intermittent faults in STT-MRAMs; it is based on monitoring the write current. The strength of the write current is inversely correlated to the write error rate; when the write current is smaller than the specification, the device is considered faulty. A reduction in the write current can be caused by any defect in the write path of the memory (e.g., interconnects and contacts). Simulation results based on industrial design show that applying DFT yields a superior coverage of intermittent faults compared to functional test methods, such as march tests.

As emerging non-volatile memory (NVM) devices, Ferroelectric Field-Effect Transistors (FeFETs) present distinctive opportunities for the design of ultra-dense and low-leakage memory systems. For matured FeFET manufacturing, it is extremely important to have an understanding of manufacturing defects and accurately model them to develop effective test solutions. This paper introduces a comprehensive framework for defect and fault modeling, which enables the development of test solutions. First, a classification of FeFET manufacturing defects is provided; both conventional defects (such as contacts and interconnect defects) as well as unique FeFET defects are discussed. The latter FeFET specific defect leads to unique faults that cannot be adequately described using traditional modeling approaches. Then, the Device-Aware Test (DAT) method is used to effectively and appropriately model, analyze and develop test solutions for such unique defects; the approach will be illustrated for Stuck-at-Polarization (SAP) defects.

Resistive Random Access Memories (RRAMs) are now undergoing commercialization, with substantial investment from many semiconductor companies. However, due to the immature manufacturing process, RRAMs are prone to exhibit new failure mechanisms and faults, which should be efficiently detected for high-volume production. Some of those faults are hard-to-detect, and require specific Design-for-Testability (DfT) circuit design. This paper proposes a DfT based on a parallel-reference write circuit that can detect all single-cell RRAM array faults: strong faults (directly causing logic errors) as well as weak faults (caused by parametric deviations). The scheme replaces the regular write driver, and enables the monitoring and comparison of the write current against multiple references during a single write operation. Hence, it serves as a DfT scheme and as a normal write circuit simultaneously. In addition, it enhances production testing speed and online fault detection, while keeping the area overhead low. Furthermore, the DfT is configurable for efficient diagnosis and yield learning. The results of the simulations performed do not only show that the DfT can detect single-cell conventional faults (due to interconnects and contacts) as well as unique RRAM faults (based on silicon data) that have been demonstrated to exist, but also that the DfT is robust to process variations.

The Spin-Transfer Torque Magnetic Random Access Memory (STT-MRAM) is on its way to commercialization. However, the development of high-quality test solutions for STT-MRAMs poses challenges due to the specific working mechanism of the core element of the STT-MRAM bit cells, i.e., the magnetic tunnel junction (MTJ), which involves both a magnetic field and spin-transfer torque. This property can introduce defects unique to MTJs which may escape from test programs that consist solely of functional write and read operations, like march tests. Hence, it is important to develop test solutions that go beyond conventional march tests. This paper explores the effect of applying an external magnetic field (Hext) on the test quality and test time of STT-MRAMs, which could be achieved by integrating one or more magnets in the Automated Test Equipment (ATE) setup. A framework for these so-called Hext-assisted tests is presented and implemented for all known conventional and unique defects. The paper demonstrates that the Hext-assisted tests offer superior coverage and/or lower test time compared to regular functional tests, like march tests. The effectiveness of these tests are validated through silicon measurements.

As electronics and software become more integrated into automobiles, Functional Safety (FuSa) per ISO 26262 becomes important. It assesses the risk level of automotive chips, reflected by the Automotive Safety Integrity Level (ASIL). Fault injection simulation verifies the FuSa of a design by injecting faults and classifying them based on whether safety mechanisms detect them. Discrepancies in classification results from FuSa EDA tools can lead to varying ASIL assignments and misrepresent associated risk. Thus, we evaluate two FuSa EDA tools, Cadence® XFS and Synopsys® VC Z01X, for RTL designs. We find that the fault space covered by the tools is not complete. Hence, we propose a novel verification methodology combining both tools to achieve maximum fault space coverage. We apply this approach to the AutoSoC benchmark suite and achieve a more accurate Diagnostic Coverage (DC) of 97.79%, over the baseline verification methodology of 98.36%, at the cost of injecting 1.31 times more faults. Our work ensures that the correct ASIL level is assigned through accurate DC estimation.

Address decoders are an integral part of random access memories. They are typically implemented using fast logic optimised for low latency. The latter, however, are difficult to test, while their repair is considered to be impossible. In this work we propose a highly scalable and testable address decoder solution, based on Content-Addressable Memories build with ferroelectric transistors (FeFET). Our solution has a transistor count close to the state of the art, while outperforms it in terms of latency. Due to its regular 2D structure, our proposal's testability is comparable to that of memory arrays. Moreover, adding a few spare rows will enable end-of-production repair, in the presence of manufacturing defects. By additionally increasing the number of address bits stored in a single FeFET CAM cell, potential area reductions of 30% - compared to the traditional dynamic NAND decoders - can be achieved.

Spin-Transfer Torque Magnetic RAMs (STT-MRAMs) are on their way to commercialization. However, obtaining high-quality test and diagnosis solutions for STT-MRAMs is challenging due to the existence of unique defects in Magnetic Tunneling Junctions (MTJs). Recently, the Device-Aware Test (DA-Test) method has been put forward as an effective approach mainly for detecting unique defecting STT-MRAMs. In this study, we propose a further advancement based on the DA-Test framework, introducing the Device-Aware Diagnosis (DA-Diagnosis) method. This method comprises two steps: a) defining distinctive features of each unique defect by characterization and physical analysis of defective MTJs, and b) utilizing march algorithms to extract distinctive features. The effectiveness of the proposed approach is validated in an industrial setting with real devices and data measurement.

Resistive Random Access Memories (RRAMs) are being commercialized with significant investment from several semiconductor companies. In order to provide efficient and high-quality test solutions to push high-volume production, a comprehensive understanding of manufacturing defects is significantly required. This paper identifies and characterizes the over-RESET phenomenon based on silicon measurements. In our case study, 30% cycles suffered from intermittent extremely high resistance state exceeding the high resistance state criteria. The paper shows the limitations of conventional defect modeling based on linear resistors. To address this challenge, the Device-Aware (DA) defect modeling method is applied; a model of the defective RRAM device is developed and calibrated using measurements to accurately describe the impact of the defect on the electrical behavior of the memory device. Afterward, fault analysis is performed based on the DA defect model, and appropriate fault models are introduced; they show that the DA defect model will sensitize deep (extremely high resistance) state faults. Finally, dedicated test solutions for over-RESET devices are proposed.

The development of Spin-transfer torque magnetic RAM (STT-MRAM) mass production requires high-quality dedicated test solutions, for which understanding and modeling of manufacturing defects of the magnetic tunnel junction (MTJ) is crucial. This paper introduces and characterizes a new defect called Back-Hopping (BH); it also provides its fault models and test solutions. The BH defect causes MTJ state to oscillate during write operations, leading to write failures. The characterization of the defect is carried out based on manufactured MTJ devices. Due to the observed non-linear characteristics, the BH defect cannot be modelled with a linear resistance. Hence, device-aware defect modeling is applied by considering the intrinsic physical mechanisms; the model is then calibrated based on measurement data. Thereafter, the fault modeling and analysis is performed based on circuit-level simulations; new fault primitives/models are derived. These accurately describe the way the STT-MRAM behaves in the presence of BH defect. Finally, dedicated march test and a Design-for-Test solutions are proposed.

Security is one of the most important features that a system must provide. Depending on the application of the target device, different threats should be considered at design time. However, the attack space is vast. Hence, it is difficult to decide what components to protect, what level of protection they require and how efficient they are in the field. This paper tries to close this validation gap for power based side channel attacks by providing a fast and reliable leakage assessment at design time that can be used to perform design space exploration for security. To accomplish our goal, we use Generative Adversarial Networks (GAN) to generate reliable power traces for hardware implementations at design time that are subsequently used to assess the leakage of the design. As a case study, we validated our framework against three AES implementations (i.e., unprotected, masked-protected, and balanced protected). In comparison to CAD-based scenarios, our findings show that the GAN model creates extremely reliable power traces in terms of attackability and leakage assessment. In addition, it is approximately 120 times quicker than CAD tools with respect to trace generation.

Next-generation personalized healthcare devices are undergoing extreme miniaturization in order to improve user acceptability. However, such developments make it difficult to incorporate cryptographic primitives using available target tech-nologies since these algorithms are notorious for their energy consumption. Besides, strengthening these schemes against side-channel attacks further adds to the device overheads. Therefore, viable alternatives among emerging technologies are being sought. In this work, we investigate the possibility of using memristors for implementing lightweight encryption. We propose a 40-nm RRAM-based GIFT-cipher implementation using a 1TIR configuration with promising results; it exhibits roughly half the energy consumption of a CMOS-only implementation. More importantly, its non-volatile and reconfigurable substitution boxes offer an energy-efficient protection mechanism against side-channel attacks. The complete cipher takes 0.0034 mm2of area, and encrypting a 128-bit block consumes a mere 242 pJ.

Resistive Random Access Memory (RRAM, or ReRAM) is a promising memory technology to replace Flash because of its low power consumption, high storage density, and simple integration in existing IC production processes. This has motivated many companies to invest in this technology. However, RRAM manufacturing introduces new failure mechanisms and faults that cause functional errors. These faults cannot all be detected by state-of-the-art test and diagnosis solutions, thus leading to slower product development and low-quality products. This paper introduces a design-for-test (DFT) based on a parallel-multi-reference read (PMRR) circuit that can detect all RRAM array faults. The PMRR circuit replaces the standard sense amplifier and compares the cell’s state to multiple references during one read operation. Thus, it can be used as a DFT scheme and a normal read circuit at once. This allows for speeding up production testing and the online detection of faults. Furthermore, the circuit is extendable so that more references can be compared, which is required for efficient diagnosis. Finally, the references can be adjusted to maximize the production yield. The circuit outperforms state-of-the-art solutions because it can detect all RRAM faults during diagnosis, production testing, and during its application in the field while minimizing yield loss.

This paper addresses one of the directions of the HORIZON EU CONVOLVE project being dependability of smart edge processors based on computation-in-memory and emerging memristor devices such as RRAM. It discusses how how this alternative computing paradigm will change the way we used to do manufacturing test. In addition, it describes how these emerging devices inherently suffering from many non-idealities are calling for new solutions in order to ensure accurate and reliable edge computing. Moreover, the paper also covers the security aspects for future edge processors and shows the challenges and the future directions.

Resistive Random Access Memory (RRAM) is a potential technology to replace conventional memories by providing low power consumption and high-density storage. As various manufacturing vendors make significant efforts to push it to high-volume production and commercialization, high-quality and efficient test solutions are of great importance. This paper analyzes interconnect and contact defects in RRAMs, while considering the impact of the memory Data Background (DB), and proposes test solutions. The complete interconnect and contact defect space in a layout-independent RRAM design is defined. Exhaustive defect injection and circuit simulation are performed in a systematic manner to derive appropriate fault models, not only for single-cell and two-cell coupling faults, but also for multi-cell coupling faults where the DBs are important. The results show the existence of unique 3-cell and 4-cell coupling faults due to e.g., the sneak path in the array induced by defects. These unique faults cannot be detected with traditional RRAM test solutions. Therefore, the paper introduces a test generation method that takes into account the DB, which is able to efficiently detect all these faults; hence, further improving the fault/defect coverage in RRAMs.

Many companies are heavily investing in the commercialization of Resistive Random Access Memories (RRAMs). This calls for a comprehensive understanding of manufacturing defects to develop efficient and high-quality test and diagnosis solutions to push high-volume production. This paper identifies and characterizes a new defect based on silicon measurements; the defect is called Ion Depletion (ID). In our case study, 45% cycles suffered from an intermittent reduction in high resistance state and did not impact low resistance state. The paper shows that the traditional fault modeling based on linear resistors as a defect model is not accurate. To address this challenge, the Device-Aware (DA) defect modeling method is applied; an RRAM model of the defective device is developed and calibrated using measurements to accurately describe the impact of the defect on the electrical behavior of the memory device. Afterward, fault analysis is performed based on the DA defect model, and appropriate fault models are introduced; they show that the ID defect may sensitize undefined state faults. Finally, dedicated test and diagnosis solutions for the ID defect are proposed.

The development of Spin-Transfer Torque Magnetic RAMs (STT-MRAMs) mass production requires high-quality test solutions. Accurate and appropriate fault modeling is crucial for the realization of such solutions. This paper targets fault modeling and test generation for all interconnect and contact defects in STT-MRAMs and shows that using the defect injection and circuit simulation for fault modeling without incorporating the impact of magnetic coupling will result in an incomplete set of fault models; hence, not obtaining accurate fault models. Magnetic coupling introduced by the stray field is an inherent property of STT-MRAMs and may foster the occurrence of additional memory faults. Not considering the magnetic coupling clearly will give rise to test escapes. The paper introduces a compact model for STT–MRAM that incorporates the intra- and inter-cell stray field, uses this model to derive the full set of fault models for interconnect and contact defects, and finally proposes an efficient test solution.

The Advanced Encryption Standard (AES) is generally regarded as one of the most popular cryptographic algorithms for ensuring data security. Typical lightweight implementations of the algorithm published in the literature focus on area and power optimization, while neglecting the performance. This paper presents a novel lightweight approach for the AES algorithm and considers both encryption and decryption. In terms of performance per unit area and performance per unit power, our 32-bit design outperforms the state-of-the-art by 1.69x and 1.27x, respectively. These improvements become even larger when implementing higher data-path designs, such as 64-bit or 128-bit designs. To enhance its resilience against side-channel attacks, we modified our design by adopting and further improving on the most recent countermeasure, i.e., Domain-Oriented Masking (DOM). The results demonstrate that our five-stage and eight-stage 1st-order DOM SBOX designs achieve a reduction in area of 9.9% and 6.9% compared to the original proposed design, respectively.

SRAM Physical Unclonable Functions (PUFs) are one of the popular forms of PUFs that can be used to generate unique identifiers and randomness for security purposes. Hence, their resilience to attacks is crucial. The probability of attacks increases when the SRAM PUF start-up values follow a predictable pattern which we refer to as bias. In this paper, we investigate the parameters impacting the SRAM PUF bias of advanced FinFET SRAM designs. In particular, we analyze the bias with respect to temperature, mismatches in the power supply network, and ramp-up time. We also consider process variation, circuit noise, and SRAM layout in our analysis. Our simulations results match with the silicon measurements. From the experiments we conclude that (i) the SRAM layout and in particular the power supply network can lead to a bias, (ii) this bias increases with temperature, and (iii) this bias increases when the supply ramp-up time decreases.

Hardware security is currently a very influential domain, where each year countless works are published concerning attacks against hardware and countermeasures. A significant number of them use machine learning, which is proven to be very effective in other domains. This survey, as one of the early attempts, presents the usage of machine learning in hardware security in a full and organized manner. Our contributions include classification and introduction to the relevant fields of machine learning, a comprehensive and critical overview of machine learning usage in hardware security, and an investigation of the hardware attacks against machine learning (neural network) implementations.

Fault injection attacks pose an important threat to security-sensitive applications, such as secure communication and storage. By injecting faults into instructions, an attacker can cause information leakage or denial-of-service. Hence, it is important to secure the sensitive parts not only by detecting faults in the executed instructions but also by correcting them. In this work, we propose a hardware detection and correction module based on Hopfield networks. Our module is connected to the instruction buffer and validates all fetched instructions. In case faults are detected, faulty instructions are replaced by corrected ones. Experimental results on a small RISC-V processor and two RSA implementations show that we achieve near perfect detection and around 70% accurate correction with 9% area overhead. This correction rate is enough to secure some implementations for all considered attacks.

SRAM Physical Unclonable Functions (PUFs) are among other things today commercially used for secure primitives such as key generation and authentication. The quality of the PUFs and hence the security primitives, depends on intrinsic variations which are technology dependent. Therefore, to sustain the commercial usage of PUFs for cutting-edge technologies, it is important to properly model and evaluate their reliability. In this work, we evaluate the SRAM PUF reliability using within class Hamming distance (WCHD) for 16nm, 14nm, and 7nm using simulations and silicon validation for both low-power and high-performance designs. The results show that our simulation models and expectations match with the silicon measurements. From the experiments, we conclude the following: (1) SRAM PUF is reliable in advanced FinFET technology nodes, i.e., the noise is low in 16nm, 14nm, and 7nm, (2) temperature variations have a marginal impact on the reliability, and (3) both low-power and high-performance SRAMs can be used as a PUF without excessive need of error correcting codes (ECCs).

The massive deployment of Internet of Things (IoT) devices makes them vulnerable against physical tampering attacks, such as fault injection. These kind of hardware attacks are very popular as they typically do not require complex equipment or high expertise. Hence, it is important that IoT devices are protected against them. In this work, we present a novel fault injection attack detector with high flexibility and low overhead. Our solution is based on the reuse of a security primitive used in many IoT devices, i.e., ring oscillator (RO) physically unclonable function (PUF). Our results show that we obtain a high detection effectiveness and no false alarms against most popular fault injection attacks based on voltage and clock manipulations.

Emerging non-volatile resistive memories like Spin-Transfer Torque Magnetic Random Access Memory (STT-MRAM) and Resistive RAM (RRAM) are in the focus of today’s research. They offer promising alternative computing architectures such as computation-in-memory (CiM) to reduce the transfer overhead between CPU and memory, usually referred to as the memory wall, which is present in all von Neumann architectures. A multitude of architectures with CiM capabilities are based on these devices, due to their inherent resistive behavior and thus their ability to perform calculation directly within the memory, and thus without invoking the CPU at all. However, emerging memories are sensitive to Process, Voltage and Temperature (PVT) variations. This sensitivity has an even larger impact on CiM architectures. In this paper, we analyze and compare the impact of PVT variations on STT-MRAM and RRAM-based CiM architectures. We perform a sensitivity analysis to identify which parts of the CiM structure are most susceptible to PVT variations, for each technology. Based on these analyses, we recommend that STT-MRAM is used in high-performance CiM, while RRAM is used for edge CiM.

Artificial neural networks (ANNs) are used to accomplish a variety of tasks, including safety critical ones. Hence, it is important to protect them against faults that can influence decisions during operation. In this paper, we propose smart and low-cost redundancy schemes that protect the most vulnerable ANN parts against fault attacks. Experimental results show that the two proposed smart schemes perform similarly to dual modular redundancy (DMR) at a much lower cost, generally improve on the state of the art, and reach protection levels in the range of 93% to 99%.

High-quality memory diagnosis methodologies are critical enablers for scaled memory devices as they reduce time to market and provide valuable information regarding test escapes and customer returns. This paper presents an efficient Hierarchical Memory Diagnosis (HMD) approach that accurately diagnoses faults in the entire memory. Faults are diagnosed hierarchically; first, their location, then their nature (i.e., static or dynamic), and finally, their functional fault model. The HMD approach leads to a more accurate diagnostic, enabling the precise identification of yield loss causes.

Testing of Computation-in-Memory (CIM) designs based on emerging non-volatile memory technologies, such as resistive RAM (RRAM), is fundamentally different from testing traditional memories. Such designs allow not only for data storage (i.e., memory configuration) but also for the execution of logical and arithmetic operations (i.e., computing configuration). Therefore, not only significant design changes are needed in the memory array and/or in the peripheral circuits, but also new fault models and test approaches are needed. Moreover, RRAM-based CIM makes use of non-linear non-volatile devices making the defect modeling with traditional linear resistor inappropriate for such device defects. Hence, even the way of doing defect modeling has to change. This paper discusses a structured test development approach for RRAM-based CIM and highlights the test challenges and how testing CIM dies is different from the traditional way of testing logic and memory. Methods for defect modeling, fault modeling, and test development will be discussed. The paper demonstrates that unique faults can occur in the CIM die while in the computation configuration and that these faults cannot be detected by just testing the CIM die in the memory configuration. Moreover, it shows that testing the CIM die in the computation configuration reduces the overall test time while improving the outgoing product quality. Finally, the paper presents an outlook on the future of structured CIM test development.

Resistive RAM (RRAM) is a promising technology to replace traditional technologies such as Flash, because of its low energy consumption, CMOS compatibility, and high density. Many companies are prototyping this technology to validate its potential. Bringing this technology to the market requires high-quality tests to ensure customer satisfaction. Hence, it is of great importance to deeply understand manufacturing defects and accurately model them to develop optimal tests. This paper presents a holistic framework for defect and fault modeling that enables the development of optimal tests for RRAMs. An overview and classification of RRAM manufacturing defects are provided. Defects in contacts and interconnects are modeled as resistors. Unique RRAM defects, e.g., forming defects, require Device-Aware defect modeling which incorporates the defect’s impact on the device’s electric properties by adjusting the affected technology and electrical parameters. Additionally, a systematic approach to define the fault space is presented, followed by a methodology to validate this space. With this methodology, accurate fault modeling for contact, interconnect, and forming defects is performed and tests are developed. The tests are able to detect all faults in a time-efficient manner, thereby proving the effectiveness of the framework. Finally, an outlook on future RRAM testing is presented.

Faster and cheaper computers have been constantly demanding technological and architectural improvements. However, current technology is suffering from three technology walls: leakage wall, reliability wall, and cost wall. Meanwhile, existing architecture performance is also saturating due to three well-known architecture walls: memory wall, power wall, and instruction-level parallelism (ILP) wall. Hence, a lot of novel technologies and architectures have been introduced and developed intensively. Our previous work has presented a comprehensive classification and broad overview of memory-centric computer architectures. In this article, we aim to discuss the most important classes of memory-centric architectures thoroughly and evaluate their advantages and disadvantages. Moreover, for each class, the article provides a comprehensive survey on memory-centric architectures available in the literature.

Understanding defects in magnetic tunnel junctions (MTJs) and their faulty behaviors are paramount for developing high-quality test solutions for STT-MRAM. This article applies the advanced device-aware test to intermediate (IM) state defects in MTJ devices based on silicon measurements and circuit simulations. An IM state manifests itself as an abnormal third resistive state, which differs from the two bi-stable states of MTJ. We performed silicon measurements on MTJ devices with diameter ranging from 60 nm to 120 nm; the results show that the occurrence probability of IM state strongly depends on the switching direction, device size, and bias voltage. We demonstrate that the conventional resistor-based fault modeling and test approach fails to appropriately model and test such a defect. Therefore, device-aware test is applied. We first physically model the defect and incorporate it into a Verilog-A MTJ compact model and calibrate it with silicon data. Thereafter, this model is used for a systematic fault analysis based on circuit simulations to obtain accurate and realistic faults in a pre-defined fault space. Our simulation results show that an IM state defect leads to intermittent write transition faults. Finally, we propose and implement a device-aware test solution to detect the IM state defect.

The popularity of perpendicular magnetic tunnel junction (pMTJ)-based spin-transfer torque magnetic random access memories (STT-MRAMs) is growing very fast. The performance of such memories is very sensitive to magnetic fields, including both internal and external ones. This article presents a magnetic-field-aware compact model of pMTJ, named the MFA-magnetic tunnel junction (MTJ) model, for magnetic/electrical co-simulation of MTJ/CMOS circuits. Magnetic measurement data of MTJ devices, with diameters ranging from 35 to 175 nm, are used to calibrate an in-house magnetic coupling model. This model is subsequently integrated into our developed compact pMTJ model, which is implemented in Verilog-A. The superiority of the proposed MFA-MTJ model for device/circuit co-design of STT-MRAM is demonstrated by simulating a single pMTJ as well as STT-MRAM full circuits. The design space is explored under PVT variations and various configurations of magnetic fields.

A novel type of hardware accelerators called automata processors (APs) have been proposed to accelerate finite-state automata. The bone structure of an AP is a hierarchical routing matrix that connects many memory arrays. With this structure, an AP can process an input symbol every clock cycle, and hence achieve much higher performance compared to conventional architectures. However, the design automation for the APs is not well researched. This article proposes a fully automated tool namedAPmapfor mapping the automata to APs that use a two-level routing matrix. APmap first partitions a large automaton into small graphs and then maps them. Multiple transformations are applied to the automaton by APmap to meet hardware constraints. The experiments on a standard benchmark suite show that our approach leads to around 19% less storage utilization compared to state-of-the-art.

Multi-Level Cell (MLC) technology can greatly reduce Resistive RAM (RRAM) die sizes to achieve a breakthrough in cost structure. In this paper, a novel design scheme is proposed to realize reliable and uniform MLC RRAM operation without the need of any read verification. MLC is implemented based on a strict control of the cell programming currents of 1T-1R HfO2-based RRAM cells. Specifically, a self-adaptive write termination circuit is proposed to control the RRAM RESET current. Eight different resistance states are obtained by varying the compliance current which is defined as the minimal current allowed by the termination circuit in the RESET direction.

The National Institute of Standard and Technology (NIST) has recently started a competition with the objective to standardize lightweight cryptography (LWC). The winning schemes will be deployed in Internet-of-Things (IoT) devices, a key step for the current and future information and communication technology market. GIFT is an efficient lightweight cipher and it is used by one-fourth of the LWC candidates in the NIST LWC competition. Thus, its security evaluation is critical. One vital threat to the security are so-called logical side-channel attacks based on cache observations. In this work, we propose a novel cache attack on GIFT referred to as GRINCH. We analyzed the vulnerabilities of GIFT and exploited them in our attack. The results show that the attack is effective and that the full key could be recovered with less than 400 encryptions.

Understanding the defects in magnetic tunnel junctions (MTJs) and their faulty behaviors are paramount for developing high-quality tests for STT-MRAM. This paper characterizes and models intermediate (IM) state defects in MTJs; IM state manifests itself as an abnormal third resistive state, apart from the two bi-stable states of MTJ. We performed silicon measurements on MTJ devices with diameter ranging from 60 nm to 120 nm; the results reveal that the occurrence probability of IM state strongly depends on the switching direction, device size, and applied bias voltage. To test such defect, appropriate fault models are needed. Therefore, we use the advanced device-aware modeling approach, where we first physically model the defect and incorporate it into a Verilog-A MTJ compact model and calibrate it with silicon data. Thereafter, we use a systematic fault analysis to accurately validate a theoretically predefined fault space and derive realistic fault models. Our simulation results show that the IM state defect causes intermittent write transition faults. This paper also demonstrates that the conventional resistor-based fault modeling and test approach fails in appropriately modeling IM defects, and hence incapable of detecting such defects.

Spiking Neural Networks (SNNs) are a strong candidate to be used in future machine learning applications. SNNs can obtain the same accuracy of complex deep learning networks, while only using a fraction of its power. As a result, an increase in popularity of SNNs is expected in the near future for cyber physical systems, especially in the Internet of Things (IoT) segment. However, SNNs work very different than conventional neural network architectures. Consequently, applying SNNs in the field might introduce new unexpected security vulnerabilities. This paper explores and identifies potential sources of information leakage for the Izhikevich neuron, which is a popular neuron model used in digital implementations of SNNs. Simulations and experiments on FPGA implementation of the spiking neurons show that timing and power can be used to infer important information of the internal functionality of the network. Additionally, the paper demonstrates that is feasible to perform a reverse engineering attack using both power and timing leakage.

Internet of things (IoT) devices are appearing in all aspects of our digital life. As such, they have become prime targets for attackers and hackers. An adequate protection against attacks is only possible when the confidentiality and integrity of the data and applications of these devices are secured. State-of-the-art solutions mostly address software and network attacks, but overlook physical/hardware attacks. Such attacks can still exploit software vulnerabilities or even introduce them. In this paper, we present embedded memory security (EMS); it protects against physical tampering of the memory of IoT devices. As a case study, we have equipped a RISC-V based system-on-chip (SoC) with an EMS module. Our experimental results show that EMS successfully can protect the SoC against hardware tampering attacks, while having a low performance overhead.

Industry is prototyping and commercializing Resistive Random Access Memories (RRAMs). Unfortunately, RRAM devices introduce new defects and faults. Hence, high-quality test solutions are urgently needed. Based on silicon measurements, this paper identifies a new RRAM unique fault, the Intermittent Undefined State Fault (IUSF); this fault causes the RRAM device to intermittently change its switching mechanism from bipolar to complementary switching, resulting in undefined state faults. First, we characterize the IUSF by analyzing RRAM devices, and demonstrate that a single RRAM device can suffer from the IUSF up to 1.068 % of its switching cycles; we relate the IUSF to two defects: capping layer doping, and over-forming. This clearly shows the importance of detecting this fault. Second, we develop a device-aware defect model that accurately describes the physical behavior of these defects and gives essential insights into the IUSF's behavior and its detection. Third, we perform fault modeling by applying the device-aware defect model, and the results are used to develop high-quality test solutions for the IUSF. The contributions in this work improve the overall RRAM test quality, which enables mass commercialization of RRAMs.

Manufacturing defects in FinFET SRAMs can cause hard-to-detect faults such as Random Read Faults (RRFs). Detection of RRFs is not trivial, as they may not lead to incorrect outputs. Undetected RRFs become test escapes, which might lead to no-trouble-found devices and early in-field failures. Therefore, the detection of RRFs is of utmost importance. This paper proposes test solutions to detect RRFs and reduce test escapes. To achieve this, we first statistically analyze the failure rate due to RRFs, followed by an experimental study of stress conditions’ (SCs) impact on detecting RRFs, such as test algorithms, supply voltage, and temperature. Based on the results, we propose a new Design-For-Testability (DFT) scheme for FinFET SRAMs to detect such faults using SCs that improve the detection rate of RRFs. This scheme introduces a negligible area and test time overhead while significantly enhancing RRF detection. Hence, using the proposed DFT leads to reduced test escapes and, consequently, higher-quality FinFET SRAMs.

STT-MRAM is one of the most promising emerging non-volatile memory technologies. As its mass production and deployment in industry is around the corner, high-quality yet cost-efficient manufacturing test solutions are crucial to ensure the required quality of products being shipped to end customers. This paper focuses on STT-MRAM testing, covering three abstraction levels: manufacturing defects, fault models, and test solutions. We first survey STT-MRAM manufacturing defect space and apply the conventional resistor-based test approach to develop test solutions. We then demonstrate with silicon measurements that this approach fails to appropriately model and test defects in STT-MRAM devices: magnetic tunnel junctions (MTJs), although it is qualified for interconnect/contact defects. Therefore, we propose a new test approach: device-aware test (DAT) to specifically target device-internal defects. We apply DAT to three key types of MTJ defects: pinhole, synthetic anti-ferromagnet flip, and intermediate state defects. After developing accurate defect models and calibrating them with silicon data, we perform comprehensive fault analyses based on SPICE circuit simulations to derive accurate and realistic fault models. Some STT-MRAM unique faults are identified, including both permanent faults and intermittent faults. Based on the obtained fault models, high-quality test solutions are proposed. Additionally, this paper also proposes a magnetic coupling model and a magnetic-field-aware compact MTJ model for fast and robust STT-MRAM designs.

Attificial neural networks are currently used for many tasks, including safety critical ones such as automated driving. Hence, it is very important to protect them against faults and fault attacks. In this work, we propose two fault injection attack detection mechanisms: one based on using output labels for a reference input, and the other on the activations of neurons. First, we calibrate our detectors during normal conditions. Thereafter, we verify them to maximize fault detection performance. To prove the effectiveness of our solution, we consider highly employed neural networks (AlexNet, GoogleNet, and VGG) with their associated dataset ImageNet. Our results show that for both detectors we are able to obtain a high rate of coverage against faults, typically above 96%. Moreover, the hardware and software implementations of our detector indicate an extremely low area and time overhead.

Asymmetric algorithms such as RSA are considered secure from an algorithmic point of view, yet their implementations are typically vulnerable as they are used by attackers to comprise the secret key. Many countermeasures have been proposed to thwart these attacks. However, they are typically broken as the key can be easily compromised when attackers succeed figuring out which part of the traces belong to the square and multiply operations. In this paper, a new countermeasure is proposed against side channel attacks, referred to as multi-bit blinding. The proposed method provides a constant execution behavior regardless of the key value without additional cost (i.e., dummy/extra operations). It realizes this by considering multiple bits of the key (i.e., two in this paper) simultaneously and always perform the same operations on them independent of the two-bit value. This makes attacks much harder as the attacker cannot retrieve the key simply by identifying the operations. Instead, the attackers need to guess the correct values of the operations as well. As a case study, the security of an RSA algorithm implementation based on the proposed method is evaluated. Our experimental results show that the new method is secure against profiled and non-profiled side channel attacks with less overhead than currently published countermeasures.

Abstract Fin Field-Effect Transistor (FinFET) technology enables the continuous downscaling of Integrated Circuits (ICs), using the Complementary Metal-Oxide Semiconductor (CMOS) technology in accordance with the More Moore domain. Despite demonstrating improvements on short channel effect and overcoming the growing leakage problem of planar CMOS technology, the continuity of feature size miniaturization tends to increase sensitivity to Single Event Upsets (SEUs) caused by ionizing particles, especially in blocks with higher transistor densities such as Static Random-Access Memories (SRAMs). Variation during the manufacturing process has introduced different types of defects that directly affect the SRAM's reliability, such as weak resistive defects. As some of these defects may cause dynamic faults, which require more than one consecutive operation to sensitize the fault at the logic level, traditional test approaches may fail to detect them, and test escapes may occur. These undetected faults, associated with weak resistive defects, may affect the FinFET-based SRAM reliability during its lifetime. In this context, this paper proposes to investigate the impact of ionizing particles on the reliability of FinFET-based SRAMs in the presence of weak resistive defects. Firstly, a TCAD model of a FinFET-based SRAM cell is proposed allowing the evaluation of the ionizing particle’s impact. Then, SPICE simulations are performed considering the current pulse parameters obtained with TCAD. In this step, weak resistive defects are injected into the FinFET-based SRAM cell. Results show that weak defects can positively or negatively influence the cell reliability against SEUs caused by ionizing particles.

Side-channel attacks (SCAs) are powerful attacks that could be used to retrieve keys from electronic devices. Several physical leakage sources can be exploited in SCAs, such as power, time, heat, and so on. Heat is one of the side-channels that is not frequently analyzed by attackers in the literature due to the high noise associated with thermal traces. This article investigates the practicality of adapting power-based SCAs [i.e., correlation power analysis (CPA) and deep-learning-based power attacks (DL-based PA)] for thermal attacks and refer to them as correlation thermal attack (CTA) and DL-based thermal attack (DL-based TA). In addition, we introduce a new attack called progressive CTA (PCTA). We evaluate the different thermal SCAs against an unprotected and protected software implementation of Rivest–Shamir–Adleman (RSA). Our results show the practicality of the three attacks (i.e. CTA, DL-based TA, and PCTA) as a 100% key recovery is realized.

Manufacturing defects can cause hard-to-detect (HTD) faults in fin field-effect transistor (FinFET) static random access memories (SRAMs). Detection of these faults, such as random read outputs and out-of-spec parametric deviations, is essential when testing FinFET SRAMs. Undetected HTD faults result in test escapes, which lead to early in-field failures. This article presents a detailed analysis of HTD faults in FinFET SRAMs by exploring their sensitization and discussing solutions to improve HTD fault coverage during manufacturing testing. We first define the fault space for SRAMs and classify all faults in the space. Following this, we perform a systematic fault analysis based on injecting resistive defects in a memory cell, inspecting its behavior, and identifying HTD faults. Furthermore, we survey existing test solutions and discuss their HTD fault coverage and limitations. Based on our analysis, it is clear that no single test solution can fully detect all HTD faults, thus leading to test escapes. Hence, there is a need for new and more efficient test solutions. Improved detection of HTD faults could be achieved by using parametric test solutions, proposing solutions that cover yet-untargeted HTD faults, combining multiple test approaches into a single solution, and further exploring stress conditions. These new approaches would reduce test escapes and therefore improve the quality of FinFET SRAMs.

Conventional computing architectures and the CMOS technology that they are based on are facing major challenges such as the memory bottleneck making the memory access for data transfer a major killer of energy and performance. Computation-in-memory (CIM) paradigm is seen as a potential alternative that could alleviate such problems by adding computational resources to the memory, and significantly reducing the communication. Memristive devices are promising enablers of a such CIM paradigm, as they are able to support both storage and computing. This paper shows the power of memristive device based CIM paradigm in enabling new efficient application-specific architectures as well as efficient implementations of some known domain-specific architectures. In addition, the paper discusses the potential applications that could benefit from such paradigm and highlights the major challenges.

The recent trends for nanoelectronic computing systems include machine-to-machine communication in the era of Internet-of-Things (IoT) and autonomous systems, complex safety-critical applications, extreme miniaturization of implementation technologies and intensive interaction with the physical world. These set tough requirements on mutually dependent extra-functional design aspects. The H2020 MSCAITN project RESCUE is focused on key challenges for reliability, security and quality, as well as related electronic design automation tools and methodologies. The objectives include both research advancements and cross-sectoral training of a new generation of interdisciplinary researchers. Notable interdisciplinary collaborative research results for the first halfperiod include novel approaches for test generation, soft-error and transient faults vulnerability analysis, cross-layer fault-tolerance and error-resilience, functional safety validation, reliability assessment and run-time management, HW security enhancement and initial implementation of these into holistic EDA tools.

Designers typically add design margins to semiconductor memories to compensate for aging. However, the aging impact increases with technology downscaling, leading to the need for higher margins. This results into a negative impact on area, yield, performance, and power consumption. As an alternative, mitigation schemes can be developed to reduce such impact. This paper proposes a mitigation scheme for the memory's sense amplifier (SA); the scheme is based on creating a skew in the relative strengths of the SA's cross-coupled inverters during design. The skew is compensated by aging due to unbalanced workloads. As a result, the impact of aging on the SA is reduced. To validate the mitigation scheme, the degradation of the sense amplifier is analyzed for several workloads. The experimental results show that the proposed mitigation scheme reduces the degradation of the sense amplifier's critical figure-of-merit, the offset voltage, with up to 26%.

Manufacturing defects can cause faults in FinFET SRAMs. Of them, easy-to-detect (ETD) faults always cause incorrect behavior, and therefore are easily detected by applying sequences of write and read operations. However, hard-to-detect (HTD) faults may not cause incorrect behavior, only parametric deviations. Detection of these faults is of major importance as they may lead to test escapes. This paper proposes a new design-for-testability (DFT) scheme for FinFET SRAMs to detect such faults by creating a mismatch in the sense amplifier (SA). This mismatch, combined with the defect in the cell, will incorrectly bias the SA and cause incorrect read outputs. Furthermore, post-silicon calibration schemes can be used to avoid over-testing or test escapes caused by process variation effects. Compared to the state of the art, this scheme introduces negligible overheads in area and test time while it significantly improves fault coverage and reduces the number of test escapes.

As a unique mechanism for MRAMs, magnetic coupling needs to be accounted for when designing memory arrays. This paper models both intra- and inter-cell magnetic coupling analytically for STT-MRAMs and investigates their impact on the write performance and retention of MTJ devices, which are the data-storing elements of STT-MRAMs. We present magnetic measurement data of MTJ devices with diameters ranging from 35 nm to 175 nm, which we use to calibrate our intra-cell magnetic coupling model. Subsequently, we extrapolate this model to study inter-cell magnetic coupling in memory arrays. We propose the inter-cell magnetic coupling factor Ψ to indicate coupling strength. Our simulation results show that Ψ≈2% maximizes the array density under the constraint that the magnetic coupling has negligible impact on the device's performance. Higher array densities show significant variations in average switching time, especially at low switching voltages, caused by inter-cell magnetic coupling, and dependent on the data pattern in the cell's neighborhood. We also observe a marginal degradation of the data retention time under the influence of inter-cell magnetic coupling.

In the recent years, cache based side-channel attacks have become a serious threat for computers. To face this issue, researches have been looking at verifying the security policies. However, these approaches are limited to manual security verification and they typically work for a small subset of the attacks. Hence, an effective verification environment to automatically verify the cache security for all side-channel attacks is still missing. To address this shortcoming, we propose a security verification methodology that formally verifies cache designs against cache side-channel vulnerabilities. Results show that this verification template is a straightforward, automated method in verifying cache invulnerability.

Today's von Neumann computing systems are facing major challenges making them not suitable for evolving ultralow power (e.g., edge computing) applications. Therefore, alternative architectures that make use of post-CMOS devices are under investigation. One of these architectures is computation-in-memory (CIM) based on memristive devices; it performs (parallel) computing within the memory core, which prevents data-movement and results in low energy consumption, at the cost of some modification in memory design. Hence, a CIM die can work either in memory configuration or in computation configuration. One implementation of this architecture is based on Scouting logic; it allows the execution of logic operations within the memory. This paper discusses fault modeling and testing of CIM architectures, applied to a Scouting logic-based architecture. It demonstrates that unique faults can occur in the CIM die while in the computation configuration, and that these faults cannot be detected by just testing the CIM die in the memory configuration, thus leading to test escapes. The paper demonstrates how an efficient test can be developed that detects all faults in both configurations. Moreover, it shows that testing the die in the computation configuration reduces the overall test time while improving the outgoing product quality.

Physically Unclonable Functions (PUFs) are security primitives that provide trustworthy hardware for key-generation and device authentication. Among them, in contrast to dedicated PUFs, intrinsic PUFs are created from existing hardware components that exploit their variability through software. In this work we focus on GPUs and present G-PUF, a PUF implemented entirely in software on CUDA and hence does not require hardware modifications. Our results show that G-PUF has comparable characteristics to SRAM and DRAM PUFs in terms of uniformity 55.61% and reliability 90.09%.

In this paper, we develop an analytical PUF model based on a compact FinFET transistor model that calculates the PUF stability (i.e. PUF static noise margin (PSNM)) for FinFET based SRAMs. The model enables a quick design space exploration and may be used to identify critical parameters that affect the PSNM. The analytical model is validated with SPICE simulations. In our experiments, we analyze the impact of process variation, technology, and temperature on the PSNM. The results show that the analytical model matches very well with the simulation model. From the experiments we conclude the following: (1) nFET variations have a larger impact on the PSNM than pFET (1.5% higher PSNM in nFET variations than pFET variations at 25°C), (2) high performance SRAM cells are more skewed (1.3% higher PSNM) (3) the reproducibility increases with smaller technology nodes (0.8% PSNM increase from 20 to 14 nm) (4) increasing the temperature from −10°C to 120°C leads to a PSNM change of approximately 1.0% for an extreme nFET channel length.

Cache attacks are one of the most wide-spread and dangerous threats to embedded computing systems' security. A promising approach to detect such attacks at runtime is to monitor the System-on-Chip (SoC) behavior. However, designing a secure SoC capable of detecting such attacks is very challenging: the monitors should be lightweight in order to avoid excessive power/energy and area costs and the attack behavior should be clearly known upfront. In this work, we present LiD-CAT, a lightweight and flexible hardware detector that is aware of leakage patterns that can be used by attackers to perform cache based attacks. LiD-CAT is a cache wrapper that implements a set of leakage properties derived from cache attacks and cache models using templates. These templates identify suspicious behavior that may lead to cache attacks. LiD-CAT is evaluated using two different cache architectures, one with a secure cache and one without. On each of them, SPEC2000 benchmarks are run together with malicious applications that execute cache attacks (i.e., Evict+Time, Prime+Probe, Flush+Reload and Flush+Flush). Results show that our lightweight detector successfully detects 99.99% of the attacks with less than 1% false-positives, has no timing penalties, and increases the area of a SoC with only 1.6%.

This paper introduces a new test approach: device-aware test (DAT) for emerging memory technologies such as MRAM, RRAM, and PCM. The DAT approach enables accurate models of device defects to obtain realistic fault models, which are used to develop high-quality and optimized test solutions. This is demonstrated by an application of DAT to pinhole defects in STT-MRAMs and forming defects in RRAMs.

Physical fault injection attacks are becoming an important threat to computer systems, as fault injection equipment becomes more and more accessible. In this work, we propose a new strategy to detect fault attacks in cryptosystems. We use a recurrent neural network (RNN) to detect problems in the program flow caused by injected faults. Our neural network is trained using the instructions of non-faulty operations and therefore, it can protect against both current and future attacks. As a case study, we use two implementations of software RSA. To test the effectiveness of our detector, we propose a collection of fault injection models, where each model represents different types of faults in the instructions. Evaluation results show that we obtain a high detection accuracy in case injected faults lead to changes in the instruction flow and hence, making it difficult to steal secrete keys. Finally, we propose an efficient hardware implementation with only a 6% area overhead compared to a RISC-V processor.

Understanding the manufacturing defects in magnetic tunnel junctions (MTJs), which are the data-storing elements in STT-MRAMs, and their resultant faulty behaviors are crucial for developing high-quality test solutions. This paper introduces a new type of MTJ defect: synthetic anti-ferromagnet flip (SAFF) defect, wherein the magnetization in both the hard layer and reference layer of MTJ devices undergoes an unintended flip to the opposite direction. Both magnetic and electrical measurement data of SAFF defect in fabricated MTJ devices is presented; it shows that such a defect reverses the polarity of stray field at the free layer of MTJ, while it has no electrical impact on the single isolated device. The paper also demonstrates that using the conventional fault modeling and test approach fails to appropriately model and test such a defect. Therefore device-aware fault modeling and test approach is used. It first physically models the defect and incorporate it into a Verilog-A MTJ compact model, which is afterwards calibrated with silicon data. The model is thereafter used for fault analysis and modeling within an STT-MRAM array; simulation results show that a SAFF defect may lead to an intermittent Passive Neighborhood Pattern Sensitive Fault (PNPSF1i) when all neighboring cells are in logic `1' state. Finally, test solutions for such fault are discussed.

Technological and architectural improvements have been constantly required to sustain the demand of faster and cheaper computers. However, CMOS down-scaling is suffering from three technology walls: leakage wall, reliability wall, and cost wall. On top of that, a performance increase due to architectural improvements is also gradually saturating due to three well-known architecture walls: memory wall, power wall, and instruction-level parallelism (ILP) wall. Hence, a lot of research is focusing on proposing and developing new technologies and architectures. In this article, we present a comprehensive classification of memory-centric computing architectures; it is based on three metrics: computation location, level of parallelism, and used memory technology. The classification not only provides an overview of existing architectures with their pros and cons but also unifies the terminology that uniquely identifies these architectures and highlights the potential future architectures that can be further explored. Hence, it sets up a direction for future research in the field.

Today's computing architectures and device technologies are unable to meet the increasingly stringent demands on energy and performance posed by emerging applications. Therefore, alternative computing architectures are being explored that leverage novel post-CMOS device technologies. One of these is a Computation-in-Memory architecture based on memristive devices. This paper describes the concept of such an architecture and shows different applications that could significantly benefit from it. For each application, the algorithm, the architecture, the primitive operations, and the potential benefits are presented. The applications cover the domains of data analytics, signal processing, and machine learning.

Memory designs typically contain design margins to compensate for aging. As aging impact becomes more severe with technology scaling, it is crucial to accurately predict such impact to prevent overestimation or underestimation of the margins. This paper proposes a methodology to accurately and efficiently analyze the impact of aging on the memory's digital logic (e.g., timing circuit and address decoder) while considering realistic workloads extracted from applications. To demonstrate the superiority of the methodology, we analyzed the degradation of the L1 data and instruction caches for an ARM v8-a processor using both our methodology as well as the state-of-the-art methods. The results show that the existing methods may significantly over-or underestimate the impact (e.g., the decoder margin up to 221% and the access time up to 20%) as compared with the proposed scheme. In addition, the results show that in general the instruction cache has the highest degradation. For example, its access time degrades up to 9% and its decoder margin up to 44%.

Automata Processor (AP) is a special implementation of non-deterministic finite automata that performs pattern matching by exploring parallel state transitions. The implementation typically contains a hierarchical switching network, causing long latency. This paper proposes a methodology to split such a hierarchical switching network into multiple pipelined stages, making it possible to process several input sequences in parallel by using time-division multiplexing. We use a new resistive RAM based AP (instead of known DRAM or SRAM based) to illustrate the potential of our method. The experimental results show that our approach increases the throughput by almost a factor of 2 at a cost of marginal area overhead.

Designers typically add design margins to memories to compensate for their aging. As the aging impact increases with technology scaling, bigger margins become necessary. However, this negatively impacts area, yield, performance, and power consumption. Alternatively, mitigation schemes can be used to reduce the impact of aging. This paper proposes a hardware-based mitigation scheme for the memory's address decoder logic. The scheme is based on adapting the decoder's workload during idle cycles by stressing the short paths and putting long paths into relaxation. Thanks to the adapted workload, the impact of aging on the address decoder is reduced, resulting in a more reliable memory. To validate the benefit of the mitigation scheme, the decoder's degradation of the L1 data and instruction caches for an ARM v8-a processor is analyzed. The experimental results show that the proposed mitigation scheme reduces the degradation of the decoder's timing margin with up to 4.1x at negligible area and no more than 3% power overhead.

Hard-to-detect faults such as weak and random faults in FinFET SRAMs represent an important challenge for manufacturing testing in scaled technologies, as they may lead to test escapes. This paper proposes a Design-for-Testability (DFT) scheme able to detect such faults by monitoring the bitline swing of FinFET memories. Using only five operations per cell, we are able to detect defects that cause deterministic, random, and weak faults. Compared to the state of the art, this leads to an improved detection capability at reduced area overhead.

The STT-MRAM manufacturing process involves not only traditional CMOS process steps, but also the integration of magnetic tunnel junction (MTJ) devices, the data-storing elements. This paper demonstrates a paradigm shift in fault modeling for STT-MRAMs by performing defect modeling and fault analysis for MTJ pinhole defects which are seen as a key type of STT-MRAM manufacturing defects. A Verilog-A compact model for defect-free MTJ devices is built and calibrated with electrical measurements on actual MTJ wafers. MTJs with a pinhole defect are extensively characterized, both during manufacturing test (t=0) and in the field (t>0), and the data is used to extend our defect-free MTJ compact model to include parameterized pinhole defects. The model is then used to perform single-cell static fault analysis and this shows not only what kind of faults can occur in an STT-MRAM, but also that the conventional fault modeling approach based on linear resistors cannot catch such behavior.

CMOS technology scaling has faced over the past recent decades significant variability and reliability challenges both from the manufacturing and operational point of view. It is well recognised that Bias Temperature Instability (BTI) is one of the most (if not the most) aging mechanisms for CMOS technology. The impact of such mechanism has been heavily studied for memory cell array of SRAMS, but not enough for peripheral circuit and its overall impact on the memory functionality. This paper quantifies the impact of BTI on the write path and read path of an SRAM while considering different supply voltages, temperatures, workloads and technology nodes. The results show that the BTI impact is marginal for the write circuitry, irrespective of the workload and technology. In contrast, the impact is much higher (~3× more) for the read path, where the sense amplifier (SA) is the most sensitive part. Therefore, a mitigation scheme for the SA is proposed and evaluated. The results show that the SA offset voltage specification can be reduced significantly (~3.5×).

This paper proposes a new test approach that goes beyond cell-aware test, i.e., device-aware test. The approach consists of three steps: defect modeling, fault modeling, and test/DfT development. The defect modeling does not assume that a defect in a device (or a cell) can be modeled electrically as a linear resistor (as the traditional approach suggests), but it rather incorporates the impact of the physical defect on the technology parameters of the device and thereafter on its electrical parameters. Once the defective electrical model is defined, a systematic fault analysis (based on fault simulation) is performed to derive appropriate fault models and subsequently test solutions. The approach is demonstrated using two memory technologies: resistive random access memory (RRAM) and spin-transfer torque magnetic random access memory (STT-MRAM). The results show that the proposed approach is able to sensitize faults for defects that are not detected with the traditional approach, meaning that the latter cannot lead to high-quality test solutions as required for a defective part per billion (DPPB) level. The new approach clearly sets up a turning point in testing for at least the considered two emerging memory technologies.

Today's computing architectures and device technologies are incapable of meeting the increasingly stringent demands on energy and performance posed by evolving applications. Therefore, alternative novel post-CMOS computing architectures are being explored. One of these is a Computation-in-Memory (CIM) architecture based on memristive devices; it integrates the processing units and the storage in the same physical location (i.e., the memory based on memristive devices). Due to their advanced manufacturing processes, use of new materials, and dual functionality, testing such chips requires specific schemes and therefore special attention. This paper describes the need for testing CIM architectures, proposes a systematic test approach, and shows the strong dependency of the test solutions on the nature of the architecture. All of these will be demonstrated using a design that is designed for computation-in-memory bit-wise logical operations.

Designers typically add design margins to compensate for chip aging. However, this leads to yield loss (in case of overestimation) or low reliability (in case of underestimation). This paper analyzes the impact of aging on a complete high-performance industrial 14-nm FinFET SRAM. It investigates the impact on the memory's parametric (i.e., its delay) and functional (i.e., correct functionality) metrics. Moreover, it examines which components are the main contributors to the degradation of the memory's reliability and how it is impacted by workload and environmental conditions, i.e., temperature and voltage fluctuations. This paper not only investigates the impact of the memory's components individually, which is typically the case in prior work, but it also studies the contribution of components' interaction to the overall memory aging. The results show that the timing circuit, address decoder, and the output latches and buffers are the main contributors to the memory's parametric degradation, while the cell, sense amplifier, and address decoder are the main contributors to its functional degradation. Moreover, the results show that it is crucial to consider the impact of the interaction of components on the aging; individual analysis leads to overly pessimistic results and even wrong conclusions in certain cases.

Memory designs usually add design margins to compensate for chip aging; this may lead to yield and performance loss (in case of overestimation) or reduced reliability (in case of underestimation). This paper analyzes the impact of aging on cutting edge high performance 14nm FinFET SRAM using a calibrated aging model; it does not only analyze the impact of the SRAM's components individually, as it is the case in prior work, but it also investigates the contribution of the interaction of these components while considering different workloads; both the overall metric of the memory (i.e., the access time) as well as metrics of individual components (e.g., sensing delay for the sense amplifier) are examined. The results show that it is crucial to consider not only the aging of all individual components, but also their interaction in order to provide accurate prediction of aging effects; considering only aging of single/individual components leads to either too optimistic or pessimistic results. For example, using our approach (which includes the components interaction) results approximately in 9.1% degradation of memory access time (for three years of aging), while using the traditional approach (based on adding the impact of individual components) results in 7.3% increase only; a relative difference of 25%, for which the timing and the address decoder components are the main contributors. With respect to individual components, the sense amplifier is the most fragile one (e.g., its offset voltage spec. degrades up to 58%).

CMOS technology and its continuous scaling have made electronics and computers accessible and affordable for almost everyone on the globe; in addition, they have enabled the solutions of a wide range of societal problems and applications. Today, however, both the technology and the computer architectures are facing severe challenges/walls making them incapable of providing the demanded computing power with tight constraints. This motivates the need for the exploration of novel architectures based on new device technologies; not only to sustain the financial benefit of technology scaling, but also to develop solutions for extremely demanding emerging applications. This paper presents two computation-in-memory based accelerators making use of emerging memristive devices; they are Memristive Vector Processor and RRAM Automata Processor. The preliminary results of these two accelerators show significant improvement in terms of latency, energy and area as compared to today's architectures and design.

Device aging is an important concern in nanoscale designs. Due to aging the electrical behavior of transistors embedded in an integrated circuit deviates from original intended one. This leads to performance degradation in the underlying device, and the ultimate device failure. This effect is exacerbated in emerging technologies. To be able to tailor effective aging mitigation schemes and improve the reliability of devices realized in cutting edge technologies, there is a need to accurately study the effect of aging in high performance industrial applications. According, this paper targets a high performance SRAM memory realized in 14nm FinFET technology and depicts how aging degrades the individual components of this memory as well as the interaction between them. Aging mitigation is critical not only from device reliability point of view but also regarding device security perspectives. It is essential to assure the security of the sensitive tasks performed by the security-sensitive circuits and to guarantee the security of information stored within these devices in the presence of aging. Accordingly in this paper, we also focus on aging-related security concerns and present the cases in which aging need to considered to preserve security.

Resistive RAM (RRAM) is one of the emerging non-volatile memories that may not only replace DRAM and/or Flash in the future, but also enable new computing paradigms such as computation-in-memory. Providing high quality and efficient test solutions are of great importance in order to enable the commercialization of such products. This paper discusses all aspects of RRAM testing including defects, fault models, test algorithms, Design-for-Testability (DFT) schemes, and future challenges. The paper highlights also the limitations and the inaccuracies of existing approaches and shows that using a linear resistor to model a defect in RRAM (as it is done today) is too pessimistic, and unable to represent the non-linear behavior of the defective RRAM devices. This may result in incorrect fault models, which in turn leads to low quality test solutions. The paper therefore also presents a novel defect modeling methodology that appropriately captures the non-linear RRAM behavior. To show its superiority, the methodology is applied to a forming defect and the results are compared with those of traditional approach.

Spin-transfer-torque magnetic RAM (STT-MRAM) is one of the most promising emerging memory technologies. As various manufacturing vendors make significant efforts to push it to the market, appropriate STT-MRAM testing is of great importance. In this paper, we demonstrate that conventional STT-MRAM defect modeling, which is based on linear resistors, is too pessimistic in representing the real nature of physical defects. It may result in incorrect fault models, which in turn can lead to low-quality test solutions. In addition, we propose a generic defect modeling methodology which captures the nonlinear behavior of STT-MRAM defects accurately; a defect is modeled by adjusting the affected STT-MRAM technology parameters. The methodology is illustrated by two examples, namely a pinhole defect and a sidewall redeposition defect, which are simulated for accurate fault modeling. In case of a pinhole defect, the STT-MRAM suffers from a fast transition between magnetic tunnel junction (MTJ) states with increased write current, making the MTJ more vulnerable to breakdown. However, with the conventional linear resistor as defect model the memory shows a slow transition or even a transition failure. Similarly, a sidewall redeposition defect causes a fast transition without current elevation, which is not observed when using the conventional approach.

The growing demand of processing power is being satisfied mainly by an increase in the number of homogeneous and heterogeneous computing cores in a system. Efficient utilization of these architectures demands analysis of memory-access behaviour of applications and perform data-communication aware mapping of applications on these architectures. Appropriate tools are required to highlight memory-access patterns and provide detailed intra- application data-communication information to assist developers in porting existing sequential applications efficiently to these architectures. In this work, we present the design of an open-source tool which provides such a detailed profile for C/C++ applications. In contrast to prior work, our tool not only reports detailed information, but also generates this information with manageable overheads for realistic workloads. Comparison with the state- of-the-art shows that the proposed profiler has, on the average, an order of magnitude less overhead as compared to the state-of-the-art data-communication profilers for a wide range of benchmarks. The experimental results show that our proposed tool generated profiling information for image processing applications which assisted in achieving a speed-up of$6.14\times$and$2.75\times$for heterogeneous multi-core platforms containing an FPGA and a GPU as accelerators, respectively.

Alternatives to CMOS logic circuit implementations are under research for future scaled electronics. Memristor crossbar-based logic circuit is one of the promising candidates to at least partially replace CMOS technology, which is facing many challenges such as reduced scalability, reliability, and performance gain. Memristor crossbar offers many advantages including scalability, high integration density, nonvolatility, etc. The state-of-the-art for memristor crossbar logic circuit design can only implement simple and small circuits. This paper proposes a mapping methodology of large Boolean logic circuits on memristor crossbar. Appropriate place-and-route schemes, to efficiently map the circuits on the crossbar, as well as several optimization schemes are also proposed. To illustrate the potential of the methodology, a multibit adder and other nine more complex benchmarks are studied; the delay, area and power consumption induced by both crossbar and its CMOS control part are evaluated.

To compensate for time-zero (due to process variation) and time-dependent (due to e.g. Bias Temperature Instability (BTI)) variability, designers usually add design margins. Due to technology scaling, these variabilities become worse, leading to the need for bigger design margins. Typically, only worst-case scenarios are considered, which will not present the actual workload of the targeted application. Alternatively, mitigation schemes can be used to counteract the variability. This paper presents a run-time design-for-reliability scheme for memory Sense Amplifiers (SAs); SAs are an integral part of any memory system and are very critical for high performance. The proposed scheme mitigates the impact of time-dependent variability due to aging by using an on-line control circuit to create a balanced workload. The simulation results show that the proposed scheme can reduce the most critical figures-of-merit, namely the offset voltage shift and the sensing delay of the SA with up to ~40% and ~10%, respectively, depending on the stress conditions (temperature, voltage, workload).

As today's CMOS technology is scaling down to its physical limits, it suffers from major challenges such as increased leakage power and reduced reliability. Novel technologies, such as memristors, nanotube, and graphene transistors, are under research as alternatives. Among these technologies, memristor is a promising candidate due to its great scalability, high integration density and near-zero standby power. However, memristor-based logic circuits are facing robustness challenges mainly due to improper values of design parameters (e.g., OFF/ON ratio, control voltages). Moreover, process variation, sneak path currents and parasitic resistance of nanowires also impact the robustness. To realize a robust design, this paper formulates proper constraints for design parameters to guarantee correct functionality of logic gates (e.g., AND). Our proposal is verified with SPICE simulations while taking both device variation and parasitic effects into account. It is observed that the errors due to analytical parameter constraints are typically within 4.5% as compared to simulations.

Traditional CMOS technology and its continuous down-scaling have been the driving force to improve performance of existing computer architectures. Today, however, both technology and computer architectures are facing challenges that make them incapable of delivering the growing computing performance requirement at pre-defined constraints. This forces the exploration of both novel architectures and technologies; not only to maintain the economic profit of technology scaling, but also to enable the computing architecture solutions for big-data and data-intensive applications. This paper discusses the emerging memristive device as a complement (or an alternative) to CMOS devices and shows how such devices enable novel computing paradigms that will solve the challenges of today's architectures for certain applications. The paper covers not only the potential of memristor devices in enabling novel memory technologies, logic design styles, and arithmetic operations, but also their potential in enabling in-memory computing and neuromorphic computing.

The CMOS technology scaling faced over the past recent decades severe variability and reliability challenges. One of the major reliability challenges is bias temperature instability (BTI). This paper analyzes the impact of BTI on the sensing delay of standard latch-type sense amplifier (SA), which is one of the critical components of high performance memories; the analysis is done by incorporating the impact of process, voltage, and temperature variations (in order to investigate the severity of the integral impact) and by considering different workloads and four technology nodes (i.e., 45, 32, 22, and 16 nm). The results show the importance of taking the SA degradation into consideration for robust memory design; the SA degradation depends on the application and technology node, and the sensing delay can increase with 184.58% for the worst case conditions at 16 nm. The results also show that the BTI impact for nominal conditions at 16 nm reaches a 12.10% delay increment. On top of that, when extrinsic conditions are considered, the degradation can reach up to 168.45% at 398 K for 16 nm.

Designers typically add design margins to compensate for time-zero variability (due to process variation) and time-dependent (due to, e.g., bias temperature instability) variability. These variabilities become worse with scaling, which leads to larger design margin requirements. As an alternative, mitigation schemes can be applied to counteract the variability. This paper investigates the impact of aging on the offset voltage of the memory's sense amplifier (SA). For the analysis, the degradation of the SAs in the L1 data and instruction caches of an ARM processor is quantified while using realistic workloads extracted from the SPEC CPU2006 Benchmark suite. Furthermore, the effect of our mitigation scheme, i.e., an online control circuit that balances the SA workload, is analyzed. The simulation results show that the mitigation scheme reduces the offset voltage degradation due to aging with up to 40% for the benchmarks, depending on the stress conditions (temperature, voltage, and workload).

Today's computer architectures suffer from many challenges, such as the near end of CMOS downscaling, the memory/communication bottleneck, the power wall, and the programming complexity. As a consequence, these architectures become inefficient in solving big data problems or general data intensive applications. Computation-in-memory (CIM) is a novel architecture that tries to solve/alleviate the impact of these challenges using the same device (i.e., the memristor) to implement the processor and memory in the same physical crossbar. In order to analyze its feasibility in depth, this paper proposes two memristor implementations of a data intensive arithmetic application (i.e., parallel addition). To the best of our knowledge, this is the first paper that considers the cost of the entire architecture including both crossbar and its CMOS controller. The results show that CIM architecture in general and the CIM parallel adder in particular have a high scalability. CIM parallel adder achieves at least two orders of magnitude improvement in energy and area in comparison with a multicore-based parallel adder. Moreover, due to a wide variety of memristor design methods (such as Boolean logic), tradeoffs can be made between the area, delay, and energy consumption.

With the continuous downscaling of CMOS technologies, ICs become more vulnerable to transistor aging mainly due to Bias Temperature Instability (BTI). This paper presents a comparative study of the BTI impact while considering varying supply voltages and temperatures for three memory sense amplifier (SA) designs: low power (LP), mid power/performance (MP), and high performance (HP). As an evaluation metric, the sensing delay (SD) of the three designs is analyzed for various workloads using 45nm technology. The results show that HP SA degrades faster than MP SA and LP SA irrespective of the workload, supply voltage, and temperature. At nominal supply voltage and temperature, HP degrades up to 1.62x faster than MP, and up to 1.94x faster than LP designs for the worst case workload. In addition, the results show that an increase of 10% in power supply has a marginal impact on the relative degradation. In contrast, the results show that a temperature increment significantly worsens the BTI impact. Finally, the results show that for 16nm technology, BTI impact becomes worse and even causes read failures. This clearly indicates that designing for reliability is not only strongly application dependent, but also technology node dependent. Hence, one has to carefully consider the targeted application, design, and technology node in order to provide appropriate solutions.

This paper investigates the impact of aging in the read path of 32nm high performance SRAM; it combines the impact on the memory cell, on the sense amplifier, and on the way they interact. The analysis is done while considering different workloads and by inspecting both the bit-line swing (which reflect the degradation of the cell) and the sensing delay (which reflects the degradation of the sense-amplifier); the voltage swing on the bit lines has a direct impact on the proper functionality of the sense amplifier. The results show that in addition to the sense amplifier degradation, the cell degradation also contributes to the sensing delay increase; the share of this contribution depends on the cell design. Moreover, this sensing delay becomes worst at stressy workloads.

The test complexity of high density DRAMs increases with technology evolution, due to a larger impact of process variation and weak defects. In particular, resistive open defects turn to be a major concern in DRAMs. Our analysis and simulation results show that an important phenomenon exists, charge accumulation, which is currently not considered in DRAM testing. Charge accumulation occurs in DRAM cells that suffer from internal resistive opens; a weak value stored at a cell is strengthened when a sequence of read operations is applied to it. Typical DRAM testing procedures (like March tests) fail to provide enhanced coverage of resistive open defects, since they do not consider charge accumulation. This paper provides an effective test algorithm that targets resistive open defects, while considering the bit-line imbalance and the charge accumulation mechanisms.

As the CMOS technology is gradually scaling down to inherent physical device limits, significant challenges emerge related to scalability, leakage, reliability, etc. Alternative technologies are under research for next-generation VLSI circuits. Memristor is one of the promising candidates due to its scalability, practically zero leakage, non-volatility, etc. This paper proposes a novel design methodology for logic circuits targeting memristor crossbars. This methodology allows the optimization of the design of logic function, and their automatic mapping on the memristor crossbar. More important, this methodology supports the execution of Boolean logic functions within constant number of steps independent of its functionality. To illustrate the potential of the proposed methodology, multi-bit adders and multipliers are explored; their incurred delay, area and energy costs are analyzed. The comparison of our approach with state-of-the-art Boolean logic circuits for memristor crossbar architecture shows significant improvement in both delay (4 to 500 x) and energy consumption (1.22 to 3.71 x). The area overhead may decrease (down to 44%) or increase (up to 17%) depending on the circuit's functionality and logic optimization level.

With the continuous downscaling of CMOS technologies, ICs become more vulnerable to transistor aging mainly due to Bias Temperature Instability (BTI). A lot of work is published on the impact of BTI in SRAMs; however most of the work focused mainly on the memory cell array. An SRAM consists also of peripheral circuitries such as address decoders, sense amplifiers, etc. This paper characterizes the combined impact of BTI and voltage temperature fluctuations on the memory sense amplifier for different technology nodes (45nm up to 16nm). The evaluation metric, the sensing delay (SD), is analyzed for various workloads. In contrast to earlier work, this paper thoroughly quantifies the increased impact of BTI in such sense amplifiers for all the relevant technology scaling parameters. The results show that the BTI impact for nominal voltage and temperature is 6.7% for 45nm and 12.0% for 16nm when applying the worst case workload, while this is 1.8% for 45nm technology and 3.6% higher for 16nm when applying the best case workload. In addition, the results show that the increase in power supply significantly reduces the BTI degradation; e.g., the degradation at -10%Vddis 9.0%, while this does not exceed 5.3% at +10%Vddat room temperature. Moreover, the results that the increase in temperature can double the degradation; for instance, the degradation at room temperature and nominal Vddis 6.7% while this goes up to 18.5% at 398K.

Memristor-based memory technology, also referred to as resistive RAM (RRAM), is one of the emerging memory technologies potentially to replace conventional semiconductor memories such as SRAM, DRAM, and flash. Existing research on such novel circuits focuses mainly on the integration between CMOS and non-CMOS, fabrication techniques, and reliability improvement. However, research on (manufacturing) test for yield and quality improvement is still in its infancy stage. This paper presents fault analysis and modeling for open defects based on electrical simulation, introduces fault models, and proposes test approaches for RRAMs. The fault analysis reveals that unique faults occur in addition to some conventional memory faults, and the detection of such unique faults cannot be guaranteed with just the application of traditional march tests. The paper also presents a new Design-for-Testability (DfT) concept to facilitate the detection of the unique faults. Two DfT schemes are developed by exploiting the access time duration and supply voltage level of the RRAM cells, and their simulation results show that the fault coverage can be increased with minor circuit modification. As the fault behavior may vary due to process variations, the DfT schemes are extended to be programmable to track the changes and further improve the fault/defect coverage.

3-D stacked integrated circuit (IC) technology based on through-silicon vias (TSVs) provides numerous advantages as compared to traditional 2-D-ICs. A potential application is memory stacked on logic, providing enhanced throughput, and reduced latency and power consumption. However, testing the TSV interconnects between the two dies is challenging as both memory and logic dies might come from different providers. Currently, no standard exists and the proposed solutions fail to address dynamic and time-critical faults (at speed testing). In addition, memory vendors have not been in favor to put additional design-for-testability structures such as Joint Test Action Group for interconnect testing on their memory devices. This paper proposes a new memory-based interconnect test (MBIT) approach for 3-D memories stacked on logic (e.g., CPUs). A structural approach is used to develop fault models, their detection conditions, and test and diagnosis patterns. The test patterns are applied by read and write instructions to the memory and are validated by a case study where a 3-D memory is assumed to be stacked on a MIPS64 processor. The main benefits of the MBIT approach are: 1) zero area overhead; 2) the ability to detect both static and dynamic faults and perform at speed testing; 3) flexibility in applying any test pattern, as this can be executed by the CPU on the logic die; 4) extreme short test execution time; and 5) the ability to perform interconnect diagnosis.

Three-Dimensional Stacked IC (3D-SIC) using Through-Silicion Vias (TSVs) is an emerging technology that provides heterogeneous integration, higher performance, and lower power consumption compared to traditional ICs. Stacking 3D-SICs using Wafer-to-Wafer (W2W) has several advantages such as high stacking throughput, high TSV density, and the ability to handle thin wafers and small dies. However, it suffers from low-compound yield as the stacking of good dies on bad dies and vice versa cannot be prevented. This article investigates wafer matching as a means for yield improvement. It first defines a complete wafer matching framework consisting of different scenarios, each a combination of a matching process (defines the order of wafer selection), a matching criterion (defines whether good or bad dies are matched), wafer rotation (defines either wafers are rotated or not), and a repository type. The repository type specifies whether either the repository is filled immediately after each wafer selection (i.e., running repository) or after all wafers are matched (i.e., static repository). A mapping of prior work on the framework shows that existing research has mainly explored scenarios based on static repositories. Therefore, the article analyzes scenarios based on running repositories. Simulation results show that scenarios based on running repositories improve the compound yield with up to 13.4% relative to random W2W stacking; the improvement strongly depends on the number of stacked dies, die yield, repository size, as well as on the used matching process. Moreover, the results reveal that scenarios based on running repositories outperform those of static repositories in terms of yield improvement at significant runtime reduction (three orders of magnitude) and lower memory complexity (from exponential to linear in terms of stack size).

Three-dimensional stacked IC (3D-SIC) technology based on Through-Silicon Vias (TSVs) provides numerous advantages as compared to traditional 2D-ICs. A potential application is memory stacked on logic, providing enhanced throughput, and reduced latency and power consumption. However, testing the TSV interconnects between the two dies is challenging, as both the memory and the logic die might come from different manufacturers. Currently, no standard exists and the proposed solutions fail to address dynamic and time-critical faults (at speed testing). In addition, memory vendors have not been in favor to put additional DfT structures such as JTAG for interconnect testing on their memory devices. This paper proposes a new Memory Based Interconnect Test (MBIT) approach for 3D stacked memories. Our test patterns are applied by read and write instructions to the memory and are validated by a case study where a 3D memory is assumed to be stacked on a MIPS64 processor. The main benefits of the MBIT approach are: (1) zero area overhead, (2) the ability to detect both static and dynamic faults and perform at speed testing, (3) flexibility in applying any test pattern, as this can be executed by the CPU on the logic die and (4) extreme short test execution time.

In order to obtain acceptable compound stack yields for 2.5D- and 3D-SICs, there is a need to test the constituting dies before stacking. The non-bottom dies of these stacks have their functional access exclusively through large arrays of fine-pitch micro-bumps, which are too dense for conventional probe technology. A common approach to obtain pre-bond test access is to equip these dies with dedicated pre-bond probe pads, which comes with drawbacks such as increased silicon area, test application time, and reduced interconnect performance. In order to avoid the many drawbacks of dedicated pre-bond probe pads, we advocate the usage of advanced probe technology that allows to directly probe on these micro-bumps. This paper reports on the technical and economical feasibility of this approach.

To fulfill customer demands, IC products must satisfy the required quality generally expressed in defective parts per million (DPPM). To meet this DPPM target, appropriate test infrastructures and test approaches must be developed. This is a challenging task for 3D Stacked-ICs (3D-SIC) due to a large test flow space; each test flow may require different design-for-test features and impact the product quality and total stack cost differently. Therefore, appropriate models to predict the impact of test flows on the product quality and overall stack cost at early design stage is important for quality versus cost trade-offs. This paper presents a model that predicts the 3D product quality in terms of DPPM for different test flows and associated cost; it incorporates the quality of the wafer manufacturing, stacking and packaging process. For example, the presented case study showed that maintaining the same product quality for larger stack size might result in a significant test cost increase.

In this paper we address lower level issues related to 3D inter-die memory repair in an attempt to evaluate the actual potential of this approach for current and foreseeable technology developments. We propose several implementation schemes both for inter-die row and column repair and evaluate their impact in terms of area and delay. Our analysis suggests that current state-of-the-art TSV dimensions allow inter-die column repair schemes at the expense of reasonable area overhead. For row repair, however, most memory configurations require TSV dimensions to scale down at least with one order of magnitude in order to make this approach a possible candidate for 3D memory repair. We also performed a theoretical analysis of the implications of the proposed 3D repair schemes on the memory access time, which indicates that no substantial delay overhead is expected and that many delay versus energy consumption tradeoffs are possible.

Recent enhancements in process development enable the fabrication of three dimensional stacked ICs (3D-SICs) such as memories based on Wafer-to-Wafer (W2W) stacking. One of the major challenges facing W2W stacking is the low compound yield. This paper investigates compound yield improvement for W2W stacked memories using layer redundancy and compares it to wafer matching. First, an analytical model is provided to prove the added value of layer redundancy. Second, the impact of such a scheme on the manufacturing cost is evaluated. Third, these two parts are integrated to analyze the trade-off between yield improvement and its associated cost; the realized yield improvement is also compared to yield gain obtained when using wafer matching. The simulation results show that for higher stack sizes layer redundancy realizes a significant yield improvement as compared to wafer matching, even at lower cost. For example, for a stack size of six stacked layers and a die yield of 85 %, a relative yield improvement of 118.79 % is obtained with two redundant layers, while this is 14.03 % only with wafer matching. The additional cost due to redundancy pays off; the cost of producing a good 3D stacked memory chip reduces with 37.68 % when using layer redundancy and only with 12.48 % when using wafer matching. Moreover, the results show that the benefits of layer redundancy become extremely significant for lower die yields. Finally, layer redundancy and wafer matching are integrated to obtain further cost reductions.

One of the key challenges in 3D Stacked-ICs (3D-SIC) is to guarantee high product quality at minimal cost. Quality is mostly determined by the applied tests and cost trade-offs. Testing 3D-SICs is very challenging due to several additional test moments for the mid-bond stacks, i.e., partially created stacks. The key question that this paper answers is what is the best test flow to be used in order to optimize the overall cost while realizing the required quality? We first present a framework covering different test flows for 3D Die-to-Wafer (D2W) stacked ICs. Thereafter, we present a cost model that allows us to evaluate these test flows. The impact of different test flows on the overall 3D-SIC cost for several die yields and stack sizes are investigated; a breakdown of the cost into test, manufacturing and packaging cost is also provided. Our simulation results show that both the test cost and the overall cost in D2W stacking strongly depends on the selected test flow; test flows with pre-bond and mid-bond stacking tests (performed during the stacking process) show a higher test cost share, but significantly reduce the overall 3D-SIC cost.

Three-Dimensional Stacked IC (3D-SIC) is an emerging technology promising many benefits, such as heterogeneous integration, reduced latency and power consumption. Realizing higher compound yield and overall low cost are the driving forces of the success of such a technology. This paper addresses these two topics. First, two yield improvement schemes will be discussed: wafer matching and layer redundancy. Wafer matching is a technique that can be applied when Wafer-to-Wafer (W2W) stacking is used to fabricate 3D-SICs, this stacking approach provides many advantages such as high throughput, thin wafer and small die handling, and high TSV density, however, it suffers from low compound yield as compared with other stacking processes. Layer redundancy, on the other hand, is based on adding redundant layer(s) to the stacked IC to replace the faulty irreparable dies in the stack. It can be applied only when similar dies are stacked as it is the case for stacked memories. Experiment results for both wafer matching and layer redundancy will be presented and compared, they show that both wafer matching and layer redundancy significantly improve the yield and therefore reduce the cost per 3D-SIC. Second, test cost optimization will be covered. During the manufacturing of 3D-SICs, tests can be applied at different moments such as before the stacking process, during the creation of each partial stacked IC, after the creation of the complete stack, etc. This results into a huge number of test flows. A framework covering different test flows will be discussed. In addition, an appropriate cost model able to identify the most cost-effective test flow will be presented. The simulation results show that test flows with the pre-bond testing significantly reduce the overall cost, that a cheaper test flow does not necessary results in lower overall cost, and that the best cost-effective test flow strongly depends on the stack yield, hence, adapting the test according the stack yield is the best approach to use.

Three-dimensional Stacked IC (3D-SIC) is a promising technology gaining a lot of attention by industry. Such technology promises lower latency, lower power consumption and a smaller footprint as compared to planar ICs. Reducing the overall 3D-SIC manufacturing cost is a major challenge driving the industry. The process of stacking the dies together is an integral part of 3D-SIC manufacturing process; hence, it impacts the overall cost. This paper introduces out-of-order stacking and compares it with the conventional in-order stacking from cost point of view. In-order stacking restricts the stacking of the dies in a bottom-up sequential order, while out-of-order stacking poses no restrictions and the order is free as long as it is realistic. The simulation results show that out-of-order stacking ends up in lower cost than in-order stacking, and that the difference increases for larger stack sizes and lower stacking yield. For example, our case study shows that for a 3D-SIC with a stack size of 6 layers, out-of-order stacking outperforms the in-order one with up to 6% using the optimal test flow.

Recent enhancements in process development enable the fabrication of three dimensional stacked ICs (3D-SICs) such as memories based on Wafer-to-Wafer (W2W) stacking. One of the major challenges facing W2W stacking is the low compound yield, especially for larger stack sizes. This paper investigates compound yield improvement for W2W stacked memories using layer redundancy. First, a analytical model is provided to prove the added value of layer redundancy. Second, the impact of such a scheme on the manufacturing cost is evaluated. Finally, these two parts are integrated to analyze the trade-off between yield improvement and its associated cost, the realized yield improvement is also compared to yield gain obtained when using wafer matching. The simulation results show that for higher stack sizes layer redundancy realizes a significant yield improvement as compared to wafer matching, and at even lower cost. For example, for a stack size of six layers and a die yield of 85%, a relative yield improvement of 82.46% is obtained using one redundant layer, while this is 10.27% with wafer matching. The additional cost due to redundancy pays off, the cost of producing a good 3D stacked memory chip reduces with 38.45% when using layer redundancy and only with 10.27% when using wafer matching. Moreover, the results show that the benefits of layer redundancy become extremely significant for lower die yields.

The industry is preparing itself for three-dimensional stacked ICs (3D-SICs), a technology that promises heterogeneous integration with higher performance and lower power dissipation at a smaller footprint. Several 3D stacking approaches are under development. From a yield point of view, Die-to-Wafer (D2W) stacking seems the most favorable approach, due to the ability of Known Good Die stacking. Minimizing the test cost for such a stacking approach is a challenging task. Every manufactured chip has to be tested, and any tiny test saving per 3D-SIC impacts the overall cost, especially in high-volume production. This paper establishes a cost model for D2W SICs and investigates the impact of the test cost for different test flows. It first introduces a framework covering different test flows for 3D D2W ICs. Subsequently, it proposes a test cost model to estimate the impact of the test flow on the overall 3D-SIC cost. Our simulation results show that (a) test flows with pre-bond testing significantly reduce the overall cost, (b) a cheaper test flow does not necessary result in lower overall cost, (c) test flows with intermediate tests (performed during the stacking process) pay off, (d) the most cost-effective test flow consists of pre-bond tests and strongly depends on the stack yield, hence, adapting the test according the stack yield is the best approach to use.

Three-Dimensional Stacked IC (3D-SIC) is an emerging technology that provides heterogeneous integration, higher performance, and lower power consumption compared to planar ICs. Fabricating these 3D-SICs using Wafer-to-Wafer (W2W) stacking has several advantages including: high throughput, thin wafer and small die handling, and high TSV density. However, W2W stacking suffers from low compound yield. This paper investigates various matching processes by using different wafer matching criteria in order to maximize the compound yield. It first establishes a framework covering different matching processes and wafer matching criteria for both replenished and non-replenished wafer repositories. Thereafter, a subset of the framework is analyzed. The simulation results show that the compound yield not only depends on the number of stacked dies, die yield, and repository size, but it also strongly depends on the used matching process and the wafer matching criteria. Moreover, by choosing an appropriate wafer matching scenario (e.g., wafer matching process, criterion etc.), the compound yield can be improved up to 13.4% relative to random W2W stacking.