Although offering great potential for energy-efficient edge-AI, memristor-based CIM accelerators are severely hindered by IR drop induced errors. To tackle this, we propose a low-cost mitigation technique by first quantifying the impact of IR drop on the accuracy. Then, a mitigation strategy is developed to compensate for IR drop-induced inference accuracy reduction by combining an optimized mapping scheme with a fine-tuned calibration of the ADC. Results show the proposed solution can effectively mitigate IR drop with a negligible overhead.

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.

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%.

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.

Traditional charge-based memories such as dynamic RAM (DRAM) and flash are facing more and more manufacturing, reliability, energy, and speed issues. A growing group of emerging memory technologies, such resistive RAM (RRAM), spin-transfer torque magnetic RAM (STT-MRAM), phase change memory (PCM), and Ferroelectric (Fe) devices (e.g., FeFET, FeRAM), address these problems. Nonetheless, these technologies are also not perfect, and thus special care must be taken to ensure that the lifecycle management, from design to obsolescence, of these memories is as optimal as possible. Lifecycle management is being developed for traditional technologies, but these are not optimized for emerging memories yet. In this paper, we present the first steps of lifecycle management for emerging memories. We analyze the different lifecycle phases that exist for two case studies on RRAM and FeFET-based memories. In this analysis, we identify how the phases affect each other and which optimizations are possible by analyzing the complete lifecycle. Finally, we compare the lifecycle phases of these two emerging memories to see how a unified approach can be developed.

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.

Abstract Memristive devices have become promising candidates to complement the CMOS technology, due to their CMOS manufacturing process compatibility, zero standby power consumption, high scalability, as well as their capability to implement high-density memories and new computing paradigms. Despite these advantages, memristive devices are susceptible to manufacturing defects that may cause faulty behaviors not observed in CMOS technology, significantly increasing the challenge of testing these novel devices after manufacturing. This work proposes an optimized Design-for-Testability (DfT) strategy based on the introduction of a DfT circuitry that measures the current consumption of Resistive Random Access Memory (ReRAM) cells to detect not only traditional but also unique faults. The new DfT circuitry was validated using a case study composed of a 3x3 word-based ReRAM with peripheral circuitry implemented based on a 130 nm Predictive Technology Model (PTM) library. The obtained results demonstrate the fault detection capability of the proposed strategy with respect to traditional and unique faults. In addition, this paper evaluates the impact related to the DfT circuitry’s introduced overheads as well as the impact of process variation on the resolution of the proposed DfT circuitry.

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.

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.

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.

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.

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.

Electronics employed in modern safety-critical systems require severe qualification during the manufacturing process and in the field, to prevent fault effects from manifesting themselves as critical failures during mission operations. Traditional fault models are not sufficient anymore to guarantee the required quality levels for chips utilized in mission-critical applications. The research community and industry have been investigating new test approaches such as device-aware test, cell-aware test, path-delay test, and even test methodologies based on the analysis of manufacturing data to move the scope from OPPM to OPPB. This special session presents four contributions, from academic researchers and industry professionals, to enable better chip quality. We present results on various activities towards this objective, including device-aware test, software-based self-test, and memory test.

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.

Emerging non-volatile resistive RAM (RRAM) device technology has shown great potential to cultivate not only high-density memory storage, but also energy-efficient computing units. However, the unique challenges related to RRAM fabrication process render the traditional memory testing solutions inefficient and inadequate for high product quality. This paper presents low-cost design-for-testability (DFT) solutions that augment the testing process and improve the fault coverage. A computation-in-memory (CIM) based DFT is realized to expedite the detection and diagnosis of faults by developing logic designs involving multi-row activation. A novel addressing scheme is introduced to facilitate the diagnosis of faults. Reconfigurable logic designs are developed to detect unique RRAM faults that offer features such as programmable reference generations, period, and voltage of operation. DFT implementations are validated on a post-layout extracted platform and testing sequences are introduced by incorporating the proposed DFTs. Results show that more than 2.3× speedup and better coverage are achieved with 6× area reduction when compared with state-of-the-art solutions.

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.

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.

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.

Abstract Complementary Metal Oxide Semiconductor (CMOS) technology has been scaled down over the last forty years making possible the design of high-performance applications, following the predictions made by Gordon Moore and Robert H. Dennard in the 1970s. However, there is a growing concern that device scaling, while maintaining cost-effective production, will become infeasible below a certain feature size. In parallel, emerging applications including Internet-of-Things (IoT) and big data applications present high demands in terms of storage and computing capability, combined with challenging constraints in terms of size, power consumption and response latency. In this scenario, memristive devices have become promising candidates to complement the CMOS technology due to their CMOS manufacturing process compatibility, great scalability and high density, zero standby power consumption and their capacity to implement high density memories as well as new computing paradigms. Despite these advantages, memristive devices are also susceptible to manufacturing defects that may cause unique faulty behaviors that are not seen in CMOS, increasing significantly the complexity of test procedures. This paper provides a review about the manufacturing process of memristives devices, focusing on Valence Change Mechanism (VCM)-based memristive devices, and a comparative analysis of the CMOS and memristive device manufacturing processes. Moreover, this paper identifies possible manufacturing failure mechanisms that may affect these novel devices, completing the list of the already known mechanisms, and provides a discussion about possible faulty behaviors. Note that the identification of these mechanisms provides insights regarding the possible memristive devices’ defective behaviors, enabling to derive more accurate fault models and consequently, more suitable test procedures.

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.

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.

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.

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.

Emerging memristor-based architectures are promising for data-intensive applications as these can enhance the computation efficiency, solve the data transfer bottleneck and at the same time deliver high energy efficiency using their normally-off/instant-on attributes. However, their storing devices are more susceptible to manufacturing defects compared to the traditional memory technologies because they are fabricated with new materials and require different manufacturing processes. Hence, in order to ensure correct functionalities for these technologies, it is necessary to have accurate fault modeling as well as proper test methodologies with high test coverage. In this paper, we propose technology specific cell-level defect modeling, accurate fault analysis and yield improvement solutions for memristor-based memory as well as Computation-In-Memory (CIM) architectures. Our overall contributions cover three abstraction levels, namely, device, architecture and system. First, we propose a device-aware test methodology in which we have introduced a key device-level characteristic to develop accurate defect model. Second, we demonstrate a yield analysis framework for memristor arrays considering reliability and permanent faults due to parametric variations and explore fault-tolerant solutions. Third, a lightweight on-line test and repair schemes is proposed for emerging CIM devices in machine learning applications.

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.

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.

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.