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 use of semiconductor devices in safety-critical scenarios is increasing in both quantity and complexity. This paper presents a novel approach to support safety requirements from RTL exploration through to implementation, with the aid of a Safety Specification Format (SSF), thereby minimizing costly development iterations and reducing the Time-To-Market. An assessment of the results is given for the CV32E40P open source RISC-V processor.

Convolutional Neural Networks (CNNs) are widely employed in various domains, including safety-critical applications such as autonomous driving. In these scenarios, the reliability of CNNs can be compromised by hardware faults occurring during inference, potentially leading to severe consequences. Evaluating the resilience of CNNs to hardware faults is primarily conducted through Fault Injection (FI) campaigns. However, a significant challenge lies in selecting an appropriate workload. Typically, the entire test set is applied for every injected fault, making the process highly time-consuming and posing difficulties for timely assessments. This paper investigates image selection strategies to rank inputs from the test dataset based on their difficulty in being classified by the CNN. The objective is to identify a minimal subset of test data that enables reliable CNN assessment while reducing computational overhead. By prioritizing challenging samples, the proposed method focuses on inputs that are more likely to reveal network vulnerabilities under fault conditions, enhancing the efficiency of the reliability evaluation process. Experimental results demonstrate that using such a subset of the test data suffices to estimate the number of critical faults for at least one image. This approach not only accelerates the reliability evaluation but also provides novel insights into CNN reliability, offering a practical framework for continuous assessments.

Microcontroller (MCU) performance screening ensures that devices meet critical specifications, such as maximum operating frequency ($F_{\max }$). On-chip Speed Monitors (SMONs), implemented as ring oscillators, provide process-correlated signals that can be used to estimate $F_{\text {max }}$ via machine learning (ML). However, traditional ML models require substantial domain expertise, extensive feature engineering, hyperparameter tuning, and dataset-specific training, limiting their scalability and generalization. In this preliminary study, we explore the use of TabPFN, a pretrained Tabular Foundation Model (TabFM) based on In-Context Learning (ICL), for MCU performance prediction. TabPFN eliminates the need for task-specific training or tuning by conditioning directly on labeled examples provided at inference time, enabling few-shot and zero-shot learning. We evaluate TabPFN on two distinct MCU datasets and compare its performance with conventional ML models, including tree-based and linear approaches. Our results show that TabPFN consistently achieves competitive accuracy with minimal human supervision, demonstrating its potential as a fast, generalizable, and low-maintenance alternative for performance screening in semiconductor manufacturing.

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%.

Traditional structural tests do not provide comprehensive coverage of all possible faults in automotive System-on-Chips (SoCs). To address these gaps, the manufacturing test flow now incorporates System-Level Test (SLT), an additional holistic test phase that executes advanced functional test programs. This research advances SLT methodologies by introducing a stress-optimization approach that targets critical, non-uniformly stressed areas, complementing structural stress methods. It also provides practical guidelines for developing SLT suites that effectively test SoC communication peripherals. Additionally, automated SLT workload generation techniques leverage graph-based SoC abstractions and Device Tree Source (DTS) files to reduce manual effort. Furthermore, grading methodologies are proposed to evaluate SLT effectiveness using high-level metrics derived from instruction traces, enabling early feedback without exhaustive fault simulation. The proposed methods are validated on a 40nm automotive SoC, manufactured by STMicroelectronics, with approximately 20 million logic gates, using a low-cost, FPGA-based modular tester. Experimental results demonstrate that SLT suites can significantly enhance the quality and reliability of automotive SoCs. Collectively, these contributions make SLT more scalable, automated, and effective, meeting stringent automotive quality standards and enabling broader application in domains such as data center processors.

Microcontroller (MCU) performance screening ensures devices meet the maximum operating frequency Fmaxspecification. Speed Monitors (SMONs), implemented as ring oscillators, are used to estimate Fmax. Traditional machine learning (ML) models have been explored for this task but require extensive feature engineering and tuning. This work investigates Tabular Foundation Models, specifically TabPFN, for MCU performance prediction. TabPFN leverages in-context learning, enabling accurate inference without dataset-specific training. We evaluate its performance on a composite dataset combining four distinct MCU product families. Results show that TabPFN matches or exceeds baseline ML models while eliminating the need for manual optimization, offering a promising direction for efficient screening in semiconductor manufacturing with minimal human supervision

A fault list analysis methodology is proposed to determine how many Cell-Aware Test (CAT) defects can be detected by test patterns generated targeting the other fault models, including stuck-at faults (SAFs), transition-delay faults (TDFs), and small-delay defects (SDDs). Our analysis reveals that a proper ordering in fault simulation can accelerate the CAT fault simulation process. We evaluated our approach on a RISC-V core, synthesized using an industrial technology library. We demonstrated an innovative method to optimize CAT fault simulation by means of preliminary static fault list analysis, resulting in fault simulation runtime reduction of up to 80% for static and 35% for dynamic CAT faults.

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.

In safety-critical applications, microcontrollers must meet stringent quality and performance standards, including the maximum operating frequency$F_{\max}$. Machine learning models have proven effective in estimating$F_{\max}$by utilizing data from on-chip ring oscillators. Previous research has shown that increasing the number of ring oscillators on board can enable the deployment of simple linear regression models to predict$F_{\max}$. However, the scarcity of labeled data that characterize this context poses a challenge in managing high-dimensional feature spaces; moreover, a very high number of ring oscillators is not desirable due to technological reasons. By modeling$F_{\max}$as a linear combination of the ring oscillators’ values, this paper employs Compressed Sensing theory to build the model and perform feature selection, enhancing model efficiency and interpretability. We explore regularized linear methods with convex/non-convex penalties in microcontroller performance screening, focusing on selecting informative ring oscillators. This permits reducing models’ footprint while retaining high prediction accuracy. Our experiments on two real-world microcontroller products compare Compressed Sensing with two alternative feature selection approaches: filter and wrapped methods. In our experiments, regularized linear models effectively identify relevant ring oscillators, achieving compression rates of up to 32:1, with no substantial loss in prediction metrics.

In safety-critical applications, microcontrollers must be compliant with the required quality constraints and performance standards, particularly in terms of the maximum operating frequency$(F_{\max })$. Machine learning (ML) models have proven effective in estimating$F_{\max }$by utilizing data extracted from on-chip ring oscillators (ROs), making them a valuable instrument for performance screening. However, the cost of obtaining labeled samples and the stringent accuracy needed by the model create hard challenges in this context. In order to address these, we explored three deep-learning (DL)-based key strategies: 1) semi-supervised learning with deep feature extractors: we leverage the abundance of unlabeled production data in a semi-supervised approach. Deep feature extractor models are employed to transform data into higher-dimensional spaces. These feature embeddings enable accurate performance prediction using simple linear regression, with a fraction of labeled data to reach baseline performances; 2) intrafamily transfer learning: when introducing new microcontroller products, with slightly different characteristics but the same set of ROs, previously trained deep feature extractors can be used, in a transfer learning fashion. This permits the use of significantly fewer labeled data compared to traditional methods; and 3) interfamily transfer learning: we extend the previous transfer learning concept to new microcontroller products with completely distinct characteristics. We aim to demonstrate that adapting the features set and fine-tuning DL feature extractors initially trained on specific legacy product data permits to yield better performance. Our research aims to provide a holistic framework for DL-based microcontroller performance screening to address the challenge of limited labeled data. The proposed methodologies significantly improve prediction accuracy and reduce the dependency on a large number of labeled samples, thus enhancing the efficiency and efficacy of ML-based microcontroller screening. The proposed framework enables models reuse, serving as a valuable baseline when new products are released.

Graphics Processing Units (GPUs) are becoming widespread, even in safety-critical applications. In that case, it is imperative to guarantee that the probability of producing critical failures due to hardware faults is lower than a given threshold. To detect possible permanent hardware faults as soon as they appear during the operational phase (e.g., due to aging), Software Test Libraries (STLs) have gained significant traction as a widely adopted test solution due to their effectiveness in terms of fault detection capabilities, test application time, and flexibility. However, a major drawback of this solution is the lack of automation in the STL generation phase. As a result, high manual labor is required for their generation. This becomes even more arduous in complex architectures that require in-depth knowledge to cover hard-to-test faults. In this article, we introduce a methodology based on Bounded Model Checking to support the generation and improvement of stuck-at-oriented STLs for hard-to-test units in GPUs, showing that we can enhance the test coverage achieved by pre-existing STLs while also identifying a set of functionally untestable faults. To experimentally validate the proposed method’s effectiveness, we use the FlexGripPlus GPU model to target two hard-to-test units, one medium to low complexity sub-unit and one high complexity sub-unit, as study cases. For both units, we had pre-existing STLs written for the stuck-at model. Resorting to the proposed method, the STLs’ test coverage was increased by 9.57% and 2.19%, respectively. In addition, the method also identified a significant number of functionally untestable faults.

Arithmetic circuits form the foundation of modern digital computation, enabling us to conduct precise mathematical operations and drive the digital age. They are integral components in nearly every digital circuit, such as processors' arithmetic and logic units. Especially in safety-critical domains like automotive and aviation, the flawless operation of these circuits is of paramount importance. This paper presents a case study involving two variants of Dadda multipliers and assesses their intrinsic reliability when affected by permanent hardware faults. We conducted extensive fault injection campaigns on the circuit models under various datasets, presenting the aggregated statistical errors in the form of the mean absolute error (MAE) for each case. Specifically, we performed fault injection campaigns in which the operands are sourced from trained quantized weights of a convolutional neural network, as well as randomly generated sets of integers. The results not only reveal differences between the two circuits but also show significant variations when different datasets are used in the fault injection campaigns.

Software-Based Self-Test (SBST) is vastly adopted as a hardware safety mechanism for the in-field test of safety-critical systems in the form of Software Test Libraries (STLs). Typically, an STL’s diagnostic coverage is evaluated on the stuck-at fault model. As various defect-oriented fault models exist and are used for manufacturing testing, such as the popular cell-aware test (CAT), there is a need to evaluate the effectiveness of SBST when such models are targeted. This work targets static CAT faults. We evaluated the fault coverage of open-available STLs for a RISC-V SoC. We used results stemming from stuck-at fault simulation and gate-exhaustive simulation to elaborate on the obtained results.

A widely adopted practice for in-field testing of electronic devices uses Software-Based Self-Test (SBST) in the form of Software Test Libraries (STLs). Typically, STLs target the stuck-at and Transition Delay Fault (TDF) models. However, to face the new defects introduced by the most recent semiconductor technologies, new fault models must be adopted. Small Delay Defects (SDDs) play an increasingly important role in this scenario. Unlike TDFs, SDDs slightly increase the paths’ timing, whose size is not in the same order of magnitude of the clock period. These defects can cause failures during the operational phase if they affect the critical paths. Remarkably, in scan testing the propagation time of a fault is limited, as a fault effect has to reach the scan flip-flops to be detected. However, in functional testing, the fault effect may require several clock cycles before reaching an observable point. Thus, the delay due to the fault cannot be indefinitely long.As there will be the need to move to delay faults when developing STLs, it is important to use the timing information correctly in functional fault simulations. SDDs are the typical choice. In this paper, we implemented a fault grading process for STLs to show how the fault coverage they can achieve changes when the delay defect increases (from SDDs to the extreme case of TDFs). The work uses static timing analysis; although this is known to yield pessimistic results in some cases, it gives a very good indication of the trend in fault coverage as the SDDs approximate TDFs. Differences in fault coverages with respect to the TDF model are highlighted, while an assessment of the effects of multi-cycle delays is also provided.

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.

In safety-critical applications, microcontrollers must meet strict quality and performance standards, including the maximum operating frequency$(F_{\max})$. Machine learning (ML) models can estimate$F_{\max}$using data from on-chip ring oscillators (ROs), making them suitable for performance screening. However, when new products are introduced, existing ML models may no longer be suitable and require updating. Training a new model from scratch is challenging due to limited data availability. Acquiring$F_{\max}$data is time-consuming and costly, resulting in a small labeled dataset. However, a large amount of data from legacy products may be available, along with existing ML models. In order to address the scarcity of labeled data, this paper proposes using deep learning feature extractors trained on specific MCU product data and fine-tuning them for new devices, in a Transfer Learning fashion. Experimental results show that these models can extract useful general features for performance prediction. As a result, they achieve better performance with significantly less labeled data compared to traditional shallow learning approaches.

In-field test of microprocessors is a major topic for the industry, especially in the safety-critical domain, where the respective standards mandate high test coverage thresholds. The dominant fault models used are the transition delay and the stuck-at fault model. However, the adoption of very advanced semiconductor technologies to manufacture devices used in safety-critical applications pushes toward considering new fault models that are better suited to catch subtle and age-related defects. Among the other phenomena, latent cell-internal defects emerged as relevant causes for several failures. Hence, the necessity for the Cell-Aware Test (CAT) was born, and the inclusion of the CAT fault model in the latest safety standards. Although CAT amends the issue of the numerous test escapes, it may suffer as well from the presence of functionally untestable faults that may pollute the overall test efficiency with their presence. In this paper, we propose a solution, based on formal methods, for the automatic identification of functionally untestable faults under the Cell-Aware fault model for the case where the DUT is a fully pipelined processor. As a case study, we used the RISC-V processor RI5CY for which we applied the minimum constraints required to ensure a functional behavior to demonstrate the effectiveness and impact of the approach. With the considered constraints, a significant percentage of functionally untestable faults was located in the several modules within the processor. Furthermore, the method allows to flexibly take into account any constraint stemming from the system configuration and the application. The obtained results have been validated by resorting to commercial EDA tools.

In the field of integrated circuit (IC) testing, the detection of defects is crucial to ensure the reliability and functionality of the final product. Among the variety of fault models that can be used to target the many possible defects in a circuit, delay faults (transition and path delay) have been used for many years. Lately, cell-aware testing (CAT) has been introduced as a different approach that aims to improve the detection of internal defects of standard cells: it involves using specific patterns to detect faults that could not be detected by common fault models (e.g., stuck-at and transition delay fault models). Both delay and cell-aware faults can be caused by several factors, such as manufacturing defects, environmental conditions, and aging effects. In this paper, we investigate the application of test patterns generated with the transition and path delay fault models in comparison with others developed with the cell-aware approach, in terms of fault coverage, pattern count and test generation time. Overall, the study shows that the combination of the path delay fault model and cell-aware testing can lead to improved fault coverage and lower test. The experimental results are presented over a wide range of open-source benchmarks and on a RISC-V design using a proprietary industrial technology library.

With the continued success of the open RISC-V architecture, practical deployment of RISC-V processors necessitates an in-depth consideration of their testability, safety and security aspects. This survey provides an overview of recent developments in this quickly-evolving field. We start with discussing the application of state-of-the-art functional and system-level test solutions to RISC-V processors. Then, we discuss the use of RISC-V processors for safety-related applications; to this end, we outline the essential techniques necessary to obtain safety both in the functional and in the timing domain and review recent processor designs with safety features. Finally, we survey the different aspects of security with respect to RISC-V implementations and discuss the relationship between cryptographic protocols and primitives on the one hand and the RISC-V processor architecture and hardware implementation on the other. We also comment on the role of a RISC-V processor for system security and its resilience against side-channel attacks.

In safety-critical applications, microcontrollers must satisfy strict quality constraints and performances in terms of Fmax(the maximum operating frequency). Data extracted from on-chip ring oscillators (ROs) can model the Fmaxof integrated circuits using machine learning models. Those models are suitable for the performance screening process. Acquiring data from the ROs is a fast process that leads to many unlabeled data. Contrarily, the labeling phase (i.e., acquiring Fmax) is a time-consuming and costly task, that leads to a small set of labeled data. This paper presents deep-learning-based methodologies to cope with the low number of labeled data in microcontroller performance screening. We propose a method that takes advantage of the high number of unlabeled samples in a semi-supervised learning fashion. We derive deep feature extractor models that project data into higher dimensional spaces and use the data feature embedding to face the performance prediction problem with simple linear regression. Experiments showed that the proposed models outperformed state-of-the-art methodologies in terms of prediction error and permitted us to use a significantly smaller number of devices to be characterized, thus reducing the time needed to build ML models by a factor of six with respect to baseline approaches.

In the domain of high reliability applications, Burn-In testing (BI) is always present since it is one of the prime countermeasures against the infant mortality phenomenon. Traditional static BI testing proves to be inefficient for modern circuit designs. As the devices’ feature size scales down and their structural and architectural complexity increases, so does the complexity and cost of the BI test. Different BI methods are employed by the industry where stimuli are also applied to the devices under test (DUTs) in order to effectively stress and stimulate all nets of the design. One known industry practice resorts to Design for Testability (DfT) infrastructures (e.g., scan) and is based on the application of test vectors at low frequency to excite the DUT as much as possible with the goal of switching each net of the design at least once. In this paper we consider the case where the layout of the circuit is known and propose two novel methods able to automatically produce functional stimuli to switch pairs of neighboring nodes (i.e., nodes that are placed within a specified distance in the DUT) in short periods of time. This solution has been shown to be able to trigger some latent defects in a circuit better than other methods. As a case study, we target functional units within a RISC-V processor (RI5CY). We show that the functional stimuli generated by the exact method described in the paper are able to achieve optimal results (i.e., the maximum functional switching of neighboring pairs), thus maximizing the chance that their at-speed application can activate weak points in the circuit.

New semiconductor technologies for advanced applications are more prone to defects and imperfections related, among several different causes, to the manufacturing process, aging, and cross-talks. These phenomena negatively affect the circuit’s timing and can be effectively modeled by means of the path delay fault (PDF) model. While path delay testing is currently supported by commercial automatic test pattern generation tools for scan designs, functional testing covering PDFs is not widely adopted, mainly because of the high cost for test generation. On the other side, functional test is already widely adopted for in-field test of stuck-at faults (SAFs), which is often performed resorting to the execution of suitable test programs (Self Test Libraries, or STLs). This approach is attractive, since it can be performed at-speed with limited time constraints and high flexibility, making it a suitable in-field test solutions. Previous work assessed the feasibility and validity of functional approaches based on test programs targeting PDFs. In this work, we present the first systematic method for the development of very high fault coverage test programs for PDFs, which largely outperform test programs written for other fault models. Moreover, the proposed method allows the identification of functionally untestable faults. The effectiveness of the proposed approach was proven on an open-source RISC-V processor core, where 100% coverage of the functionally testable longest paths was achieved, compared with an initial coverage of 0.52% achieved with test programs targeting SAFs. Results demonstrate that shorter paths are also effectively covered.

In safety-critical applications, microcontrollers have to be tested to satisfy strict quality and performance constraints. It has been demonstrated that on-chip ring oscillators can be used as speed monitors to reliably predict the performances. However, any machine-learning (ML) model is likely to be inaccurate if trained on an inadequate dataset, and labeling data for training is quite a costly process. In this article, we present a methodology based on active learning to select the best samples to be included in the training set, significantly reducing the time and cost required. Moreover, since different speed measurements are available, we designed a multilabel technique to take advantage of their correlations. Experimental results demonstrate that the approach halves the training-set size, with respect to a random-labeling, while it increases the predictive accuracy, with respect to standard single-label ML models.

Throughout device testing, one key parameter to be considered is the switching activity (SWA) of the circuit under test (CUT). To avoid unwanted scenarios due to excessive power consumption during test, in most cases the SWA of the CUTs must be retained to a minimal value when the test stimulus is applied. However, there are specific cases where the opposite, namely, the SWA maximization within the CUT, or a certain submodule of it, can be proven beneficial. For example, during dynamic burn-in testing we aim at maximizing the internal stress by applying suitable stimuli. This can be done in a functional manner by following the software-based self-test paradigm. However, generating such suitable programs represents a costly and arduous task for the test engineers. We consider the case where the CUT is a pipelined processor core and we aim to maximize the SWA of certain core submodules. We present a comprehensive methodology based on formal methods, able to automatically generate the best two-instruction stress-inducing sequence for the targeted processor module. The generated stimulus is composed of a short, arbitrarily long repeatable sequence of a pair of assembly instructions, thus, guaranteeing the maximum possible constant SWA. The proposed method was applied to the OpenRISC 1200 and the RI5CY (PULP) processor cores demonstrating its effectiveness when compared to other methods. We show that the time for generating the best repeatable instruction sequence is limited in most cases, while the generated sequence can always achieve a significantly higher repeatable and constant SWA than other solutions.

Graphics Processing Units (GPUs) boost the development of high-performance safety-critical applications. The reliability of such systems is of utmost importance since faults affecting the hardware may occur at any time during the systems' operational life. Thus, methods to effectively test these devices during their in-field operation are necessary. One popular solution relies on Software Test Libraries (STLs), which recently have been started being used for G PU s as well, since they are effective in terms of fault detection capabilities, intrusiveness, flexibility, and test duration. A drawback of the STL approach for G PU s is the extensive effort used to develop effective test routines for complex structures, e.g., controllers, due to the complicated constraints stemming from the ISA, the available compilation flows and parallelism constraints. We propose a novel technique based on formal methods to support the generation of stimuli and enhance the quality of pre-existing STLs for GPUs. To validate the proposed method, we resort to an open-source GPU model (Flex GripPlu s). Experimental results show that the method can effectively generate complementary code fragments to be added to existing STLs and increase their fault coverage. In the case of the GPU's decoding unit, the stuck-at fault coverage was increased by nearly 10%.

This paper encompasses three contributions by industry professionals and university researchers. The contributions describe different trends in automotive products, including both manufacturing test and run-time reliability strategies. The subjects considered in this session deal with critical factors, from optimizing the final test before shipment to market to in-field reliability during operative life.

Embedded memories in Automotive Systems-on-Chip usually occupy a large die area portion. Consequently, their defectivity can strongly impact production yield for any automotive device. Along with the technology ramp-up phase and for statistical process control reasons during volume production, it is a good automotive industry practice to collect diagnostic information in addition to pure testing data. Designers and technology experts must receive accurate diagnostic results from failing devices to react to misbehavior by identifying and correcting the related issues at their source and drawing correct repair strategy conclusions. A commonly used approach resorts to the generation of failure bitmaps based on collecting all failing bits coordinates to be sent one by one to the tester. More efficiently, the encountered faults can be compacted or compressed in on-chip memory resources to be retrieved by the tester at the end of the memory test.This paper presents an on-chip method to compact diagnostic information during embedded memory testing. More specifically, the method is applied to diagnose embedded FLASH memories. This strategy permits the reconstruction of failure bitmaps without any loss, while compression approaches obtain an approximation. The proposed method uses a fraction of the memory requested by a coordinate-based bit mapping approach and is comparable to compression methods. At the cost of a moderate test time overhead, the proposed strategy permits dramatically increasing the number of devices that can be fully diagnosed without any bitmap reconstruction loss. Most failing devices in a real embedded FLASH production scenario were diagnosed after a single transfer from on-chip to the tester host computer.

In-field test of integrated circuits using Self-Test Libraries (STLs) is a widely used technique specifically suited to guarantee the processor’s correct behavior during the operative lifetime, as mandated by functional safety standards such as ISO26262. Developing STLs for stuck-at faults requires significant manual efforts from test engineers, and targeting delay faults is even more challenging. In order to support this process, in this paper we propose a method to automate the creation of STLs targeting delay faults starting from existing STLs targeting stuck-at faults. The method is based first on identifying excited but not-observed transition delay faults and then adding suitable instructions able to detect them. Experimental results on a RISC-V processor show that the method can systematically detect a significant percentage of the target faults with reasonable computational effort and test code size increase.

In safety-critical applications, microcontrollers must satisfy strict quality constraints and performances in terms of $F_{\max}$, that is, the maximum operating frequency. It has been demonstrated that data extracted from on-chip speed monitors can model the $F_{\max}$ of integrated circuits by means of machine learning models, and that those models are suitable for the performance screening process. However, while acquiring data from these monitors is quite an accurate process, the labelling is time-consuming, costly, and may be subject to different measurements errors, impairing the final quality. This paper presents a methodology to cope with anomalous and noisy data in the context of the multi-label regression problem of microcontroller performance screening. We used outlier detection based on Inter Quartile Range (IQR) and Z-score and imputation techniques to detect errors in the labels and to avoid to drop incomplete samples, building higher-quality training set for our models, optimizing the devices characterization phase. Experiments showed that the proposed methodology increases the performance of existing models, making them more robust. These techniques permitted us to use a significantly smaller number of samples (about one third of the devices available for characterization), thus making the costly data acquisition process more efficient.

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.

Functional test using a Software Test Library (STL) is becoming a standard solution for the in-field test of safety-critical systems, in compliance with functional safety standards, such as the ISO26262 for the automotive domain. However, developing high-quality test programs is considerably more challenging than generating scan test patterns through commercial tools, mainly due to the lack of mature EDA tools. As a result, in many cases, the effort needed to reach the target fault coverage is not affordable. In this paper, we propose a methodology to improve the fault coverage of an STL using already available hardware resources. The proposed approach identifies the set of sequential cells that capture fault effects before being masked during their propagation towards observable points. Using a heuristic set covering approach, we select the subset of flip-flops needed to reach the target fault coverage, and exploit post-silicon debug hardware to make fault effects observable. Experimental results gathered on an open-source RISC-V core show significant improvements in the stuck-at and delay fault coverage values.

During device testing, one of the aspects to be considered is the minimization of the switching activity of the circuit under test in order to steer clear of introducing problems due to device overheating. Nevertheless, there are also certain scenarios during which the maximization of switching activity of the circuit under test (CUT) or of certain parts of it could be proven beneficial e.g., during Burn-In (BI), where internal stress is often produced by applying suitable stimuli. This can be done in a functional manner based on Software-based Self-Test in order to avoid possible damages to the CUT and/or any kind of yield loss. However, the generation of suitable test programs for this task represents a non-trivial task. In this paper we consider a scenario where the circuitry to be stressed is a pipelined processor. We present a methodology, based on formal techniques, able to automatically generate the best functional stress stimuli, i.e., a short and repeatable sequence of assembly instructions, which is guaranteed to induce the maximum switching activity within a given target processor module over a pre-defined time period. For the purposes of our experiments we used the OpenRISC 1200. The gathered experimental results demonstrate the effectiveness of the developed method. In particular, we show that the time for generating the best instruction sequence is limited in most cases, while the generated sequence can always achieve a significantly higher sustained toggling activity than any other solution.

One key aspect to be considered during device testing is the minimization of the switching activity of the circuit under test (CUT), thus avoiding possible problems stemming from overheating it. But there are also scenarios, where the maximization of certain circuits' modules switching activity could be proven useful (e.g., during Burn-In) in order to exercise the circuit under extreme operating conditions in terms of temperature (and temperature gradients). Resorting to a functional approach based on Software-based Self-test guarantees that the high induced activity cannot damage the CUT nor produce any yield loss. However, the generation of effective suitable test programs remains a challenging task. In this paper, we consider a scenario where the modules to be stressed are sub-modules of a fully pipelined processor. We present a technique, based on an evolutionary approach, able to automatically generate stress test programs, i.e., sequences of instructions achieving a high toggling activity in the target module. With respect to previous approaches, the generated sequences are short and repeatable, thus guaranteeing their easy usability to stress a module (and increase its temperature). The processor we used for our experiments is the Open RISC 1200. Results demonstrate that the proposed method is effective in achieving a high value of sustained toggling activity with short (3 instructions) and repeatable sequences.

Speed monitors provide on-chip measurements of the the performance of integrated circuits. In recent years, they have been extensively used to predict Fmaxof microcontrollers for speed binning and performance screening during production test. However, while the use of machine learning is getting increasingly popular, the models may become significantly inaccurate if not trained on the appropriate devices. Previous research has demonstrated how to predict performance from speed-monitor data using corner-lot wafers. We show how to extend this approach to select the best corner-lot wafers to label when preparing the training set, thus significantly reducing the time and cost required for the process.

Testing digital integrated circuits is generally done using Design-for-Testability (DfT) solutions. Such solutions, however, introduce non-negligible area and timing overheads that can be overcome by adopting functional solutions. In particular, functional test of integrated circuits plays a key role when guaranteeing the device's safety is required during the operative lifetime (in-field test), as required by standards like ISO26262. This can be achieved via the execution of a Self-Test Library (STL) by the device under test (DUT). Nevertheless, developing such test programs requires a significant manual effort, and can be non-trivial when dealing with complex modules. This paper moves the first step in defining a generic and systematic methodology to improve transition delay faults' observability of existing STLs. To do so, we analyze previously devised STLs in order to highlight specific points within test programs to be improved, leading to an increase in the final fault coverage.

In several test and reliability problems (from test generation to FMECA and Burn In) it is important to preliminarily identify those lines in a circuit netlist, which can not be controlled, i.e., can not be toggled to both logic values no matter the applied stimuli. Several techniques have been proposed in the past to attack this problem. In this paper we consider the case where the circuit is a pipelined processor, discuss the specific challenges of this scenario and propose some techniques to automatically identify some of the uncontrollable lines. The approach we devised uses SAT solving as underlying technology. We report the results we gathered on the OR1200 processor, showing that our method allows to trade off between the required computational effort and the achieved results. When compared with results produced by a commercial tool, our approach is able to identify a much higher number of uncontrollable lines with reasonable computational requirements.

The development of Integrated Circuits for the Automotive sector imposes on major challenges. ISO26262 compliance, as part of this process, entails complex analysis for the evaluation of potential random hardware faults. This paper proposes a systematic approach to identify faults that do not disrupt safety-critical functionalities and consequently can be considered Safe. By deploying code coverage and Formal verification techniques, our methodology enables the classification of faults that are unclassified by other technologies, improving ISO26262 compliance. Our results, in combination with Fault Simulation, achieved a Diagnostic Coverage of 93% in a CAN Controller. These figures allow an initial assessment for an ASIL B configuration of the IP.

Path Delay fault test currently exploits DfT-based techniques, mainly relying on scan chains, widely supported by commercial tools. However, functional testing may be a desirable choice in this context because it allows to catch faults at-speed with no hardware overhead and it can be used both for end-of-manufacturing tests and for in-field test. The purpose of this article is to compare the results that can be achieved with both approaches. This work is based on an open-source RISC-V-based processor core as benchmark device. Gathered results show that there is no correlation between stuck-at and path delay fault coverage, and provide guidelines for developing more effective functional test.

During the manufacturing process, electronic devices are thoroughly tested for defects. However, testing for well-known fault models, such as stuck-at and transition delay, may not be sufficient for an effective performance screening. In modern devices, Design-for-Testability features embedded at design time can allow the tester to apply stimuli and measure different critical parameters. We propose to use some of these structures, namely the speed monitors, to predict the maximum operating speed, and screen out under-performing devices. We design a complete methodology, from the extraction of robust labels, through a machine-learning algorithm, down to a post-processing step, able to meet the quality standards imposed by industry. Experimental results using real production data demonstrate the feasibility of the approach.

The Controller Area Network (CAN) bus is a serial bus protocol widely used in the automotive domain to allow communication between different Electronic Control Units in the car. Being often part of safety-critical systems, the hardware implementing the CAN network must be constantly tested along the system lifetime, even during the operational phase. CAN controllers are relatively complex modules in charge of managing the sending and the receiving of packages through the CAN bus and defects affecting them can easily compromise the whole CAN network. In this work, the CAN controller is tested by test programs to be executed by the CPU connected to the device under test and by another unit connected to the same CAN bus. A fault grading with respect to structural permanent faults of a functional test based on the execution of a software test library for the CAN bus is presented for the first time. Results show how the approach can cover more than 90% of stuck-at faults on an open-source implementation of the standard, which is significantly more than what a usual functional test based on some sample application can achieve.

With the complexity of nanoelectronic devices rapidly increasing, an efficient way to handle large number of embedded instruments became a necessity. The IEEE 1687 standard was introduced to provide flexibility in accessing and controlling such instrumentation through a reconfigurable scan chain. Nowadays, together with testing the system for defects that may affect the scan chains themselves, the diagnosis of such faults is also important. This article proposes a method for generating stimuli to precisely identify permanent high-level faults in a IEEE 1687 reconfigurable scan chain: the system is modeled as a finite state automaton where faults correspond to multiple incorrect transitions; then, a dynamic greedy algorithm is used to select a sequence of inputs able to distinguish between all possible faults. Experimental results on the widely-adopted ITC'02 and ITC'16 benchmark suites, as well as on synthetically generated circuits, clearly demonstrate the applicability and effectiveness of the proposed approach: generated sequences are two orders of magnitude shorter compared to previous methodologies, while the computational resources required remain acceptable even for larger benchmarks.

Today, safety-critical applications require self-tests and self-diagnosis approaches to be applied during the lifetime of the device. In general, the fault coverage values required by the standards (like ISO 26262) in the whole System-on-Chip (SoC) are very high. Therefore, different strategies are adopted. In the case of the processor core, the required fault coverage can be achieved by scheduling the periodical execution of a set of test programs or Software-Test Library (STL). However, the STL for infield testing should be able to comply with the operating system specifications without affecting the mission operation of the device application. In this paper, the most relevant problems for the development of the STL are first discussed. Then, it presents a set of strategies and solutions oriented to produce an efficient and non-intrusive STL to be used exclusively during the in-field testing of automotive processor cores. The proposed approach was experimented on an automotive SoC developed by STMicroelectronics.

The IEEE 1687 standard introduces several novelties, most notably Reconfigurable Scan Networks (RSNs), i.e., scan chains whose length can change dynamically. These architectures offer important advantages but can result in extremely complex integrity test following traditional structural approaches. In this paper, we will present an innovative approach to RSN test and debug based on the functional features of the standard, which is able to greatly speed up test generation time while guaranteeing a precise fault coverage.

The growing need for effectively accessing registers (called instruments) related to non-functional purposes (e.g., test, debug, calibration) in many electronic devices pushed towards the development of new solutions, including the IEEE 1687 standard. The approach supported by these solutions allows a flexible access to embedded instruments through the Boundary Scan interface via a set of reconfigurable scan chains composing a Reconfigurable Scan Network (RSN). Since permanent faults may affect the circuitry implementing them, several works recently proposed techniques to automatically generate a suitable sequence of input stimuli able to detect them. The common approach is based on forcing the IEEE 1687 network to undergo a sequence of test sessions, each composed of a configuration phase and a test phase. By properly selecting the sequence of network configurations to be used, we can guarantee that the method can test any permanent fault possibly affecting the network. Clearly, the cost of this test directly depends on its duration. This paper faces the issue of generating a test sequence for a generic RSN possibly reducing its duration and proposes a method based on an evolutionary algorithm. We provide some experimental results gathered on the standard set of benchmarks RSNs, showing that the approach is able to produce optimized test sequences in 9 cases out of 16. In some cases, the reduction in test time is larger than 20%.

Software-based self-test (SBST) is being widely used in both manufacturing and in-the-field testing of processor-based devices and Systems-on-Chips. Unfortunately, the stuck-at fault model is increasingly inadequate to match the new and different types of defects in the most recent semiconductor technologies, while the explicit and separate targeting of every fault model in SBST is cumbersome due to the high complexity of the test-generation process, the lack of automation tools, and the high CPU-intensity of the fault-simulation process. Moreover, defects in advanced semiconductor technologies are not always covered by the most commonly used fault-models, and the probability of defect-escapes increases even more. To overcome these shortcomings we propose the first fault-independent SBST method. The proposed method is almost fully automated, it offers high coverage of non-modeled faults by means of a novel SBST-oriented probabilistic metric, and it is very fast as it omits the time-consuming test-generation/fault-simulation processes. Extensive experiments on the OpenRISC OR1200 processor show the advantages of the proposed method.

The broad need to efficiently access all the instrumentation embedded within a semiconductor device called for a standardization, and the reconfigurable scan networks proposed in IEEE 1687 have been demonstrated effective in handling complex infrastructures. At the same time, different techniques have been proposed to test the new circuitry required; however, most of the automatic approaches are either too computationally demanding to be applied in complex cases, or too approximate to yield high-quality tests. This paper models the state of a reconfigurable scan network with a finite state automaton, using the length of the active path as the output alphabet and the configurations as input symbols. Permanent faults are represented as incorrect transitions, and a greedy algorithm is used to generate a functional test sequence able to detect all these multiple state-transition faults. The automaton's state set and the input alphabet are small subsets of the possible ones, and are carefully chosen. Experimental results on ITC'16 benchmarks demonstrate that the proposed approach is broadly applicable; the test sequences are more efficient than the ones previously generated by search heuristics.

Nowadays, industries require reliable methods for accessing the instrumentations embedded within semiconductor devices. The situation led to the definition of standards, such as the IEEE 1687, for designing the required infrastructures, and the proposal of techniques to test them. So far, most of the test-generation approaches are either too computationally demanding to be applied in complex cases, or too approximate to yield high-quality tests. This paper exploits a recent idea: the state of a generic reconfigurable scan chain is modeled as a finite state automaton and a low-level fault, as an incorrect transition; it then proposes a new algorithm for generating a functional test sequence able to detect all incorrect transitions far more efficiently than previous ones. Such an algorithm is based on a greedy search, and it is able to postpone costly operations and eventually minimize their number. Experimental results on ITC`16 benchmarks demonstrate that the proposed approach is broadly applicable; has limited computational requirements; and the test sequences are order of magnitudes shorter than the ones previously generated by approximate methodologies.

One of the current trends in space electronics is towards considering the adoption of COTS components, mainly to widen the spectrum of available products. When substituting space-qualified components with COTS ones a major challenge lies in guaranteeing the same level of reliability. To achieve this goal, a mix of different solutions can be considered, including effective test techniques, able to guarantee a high level of permanent fault coverage while matching several constraints in terms of system accessibility and hardware complexity. In this paper, we describe an approach based on Software-based Self-test, which is currently being adopted within the MaMMoTH-Up project, targeting the development of an innovative COTS-based system to be used on the Ariane5 launcher. The approach aims at testing the OR1200 processor adopted in the system, combined with new and effective techniques for identifying the safe faults. Results also include a comparison between functional and structural test approaches.

Modern devices often include several embedded instruments, such as BIST interfaces, sensors, calibration facilities. New standards, such as IEEE Std 1687, provide vehicles to access these instruments. In approaches based on reconfigurable scan networks (RSNs), instruments are coupled with scan registers, connected into chains and interleaved with reconfigurable modules. Such modules embed reconfigurable multiplexers that permit a selective access to different parts of the chain. A similar scenario is also supported by IEEE Std 1149.1-2013. The test of permanent faults affecting an RSN requires to shift test vectors throughout a certain number of network configurations. This paper presents some methodologies to select the list of configurations that perform the complete test of the reconfigurable modules of the RSN. In particular, one method is presented that, by construction, can be proved to be able to apply the test in the minimum amount of clock cycles. Other methods are sub-optimal in terms of test application time (TAT), but scale well on large circuits. In order to provide a comparison between the proposed methods, experimental results on some benchmark RSNs are provided.

Modern devices often include several embedded instruments, such as BISTs, sensors, and other analog components. New standards, such as IEEE Std. 1687, provide vehicles to access these instruments. In approaches based on reconfigurable scan networks, instruments are coupled with scan registers, connected into chains and interleaved with reconfigurable multiplexers, permitting a selective access to different parts of the chain. A similar scenario is also supported by IEEE Std. 1149.1-2013, where a test data register can be constructed as a chain of multiple segments, some of which can be excluded or mutually selected. The test of permanent faults affecting a reconfigurable scan network requires to shift test patterns throughout a certain number of network configurations. This paper presents a method to select the list of configurations needed to apply the complete test set in the minimum amount of clock cycles. The method is based on a graph representation ofthe problem. Experimental results on some benchmark networks are provided, together with a comparison with other approaches based on heuristics. The provided results can be effectively usedto evaluate the test time of sub-optimal approaches.

The IEEE 1687 standard describes reconfigurable structures allowing to flexibly access the instruments existing within devices (e.g., to support test, diagnosis, calibration, etc.), by using configuration modules which act as controllable switches. The increasing adoption of this standard requires the availability of algorithms and tools to automate its usage. The resulting networks might be affected by defects preventing their correct operation. This necessitates the availability of solutions which allow not only to test against defects, but also to identify the location of possible faults via diagnosis. This paper for the first time addresses the problem of the diagnosis of IEEE 1687 networks. Experimental results gathered on a set of benchmark networks show the feasibility of the solution and provide a first idea about the length of the required input stimuli.

The IEEE 1687 standard describes reconfigurable structures allowing to flexibly access the instruments existing within devices (e.g., to support test, debug, calibration, etc.), by the use of configurable modules acting as controllable switches. The increasing adoption of this standard requires the availability of algorithms and tools to automate its usage. Since the resulting networks could inevitably be affected by defects which may prevent their correct usage, solutions allowing not only to test against these defects, but also to diagnose them (i.e., to identify the location of possible faults) are of uttermost importance. This paper proposes a method to automatically generate suitable test stimuli: by applying them and observing the output of the network one can not only detect possible faults, but also identify the fault responsible for the misbehavior. Experimental results gathered on a set of benchmark networks with a prototypical tool implementing the proposed techniques show the feasibility and provide a first idea about the length of the required input stimuli.

The saturation of the IJTAG concept and its approval as the IEEE 1687 standard in 2014 has generated a wave of research activities and created demand for a set of appropriate and challenging benchmarks. This paper presents such a set developed by an industrial and academic consortium and constructed in a way that facilitates objective comparison of experimental results across research groups as well as represents challenging network examples exhaustively utilizing features and constructs defined by the standard. The suite is arranged in four comprehensive categories, each having its particular purpose and composition principles, as described in the paper. We have also made an analysis of limitations of previous popular and ad-hoc benchmark sets as these limitations majorly motivated our current action. The new public-domain benchmarks are distributed together with source files and documentation through the dedicated web site. Some of the previous research results on IEEE 1687 have been reapplied on the new benchmarks set, thus creating an important initial reference point for the research community.

Thermal phenomena occurring along test execution at the final stages of the manufacturing flow are considered as a significant issue for several reasons, including dramatic effects like circuit damage that is leading to yield loss. This paper tries to redeem those bad guys in order to exploit them to improve the test quality, reducing the overall test cost without affecting the yield.

Functional test and software-based self-test (SBST) approaches for processors are becoming popular as they enable low-cost production tests and are often the only solution for in-field tests. With the increasing use of volume diagnosis, efficient and cost-effective diagnosis methods are required. A high quality functional or SBST test program can be used to perform logic fault diagnosis with low-cost test equipment and therefore significantly reduce the cost of diagnosis. We present a framework for the automatic generation of functional diagnostic sequences for stuck-at faults. The framework allows a user to specify constraints imposed by the employed test environment and generates diagnostic sequences satisfying these constraints. Furthermore, the framework is able to prove the equivalence of faults under the specified constraints. This enables to compute the best possible diagnostic quality that can be reached under the given environmental constraints. Also, it gives the necessary information for implementing selective DFT techniques in order to differentiate faults which cannot be distinguished otherwise. In our experiments we evaluated a MIPS-like processor. The results show that our approach can effectively distinguish fault pairs or prove their equivalence, under different environmental constraints. To the best, of our knowledge, this is the first approach which, enables the automatic generation of diagnostic SBST, programs and allows to eectively prove the equivalence of faults in functional and SBST test environments.

Software-Based Self-Test is an effective methodology for devising the online testing of Systems-on-Chip. In the automotive field, a set of test programs to be run during mission mode is also called Core Self-Test library. This paper introduces many new contributions: (1) it illustrates the several issues that need to be taken into account when generating test programs for on-line execution; (2) it proposed an overall development flow based on ordered generation of test programs that is minimizing the computational efforts; (3) it is providing guidelines for allowing the coexistence of the Core Self-Test library with the mission application while guaranteeing execution robustness. The proposed methodology has been experimented on a large industrial case study. The coverage level reached after one year of team work is over 87 percent of stuck-at fault coverage, and execution time is compliant with the ISO26262 specification. Experimental results suggest that alternative approaches may request excessive evaluation time thus making the generation flow unfeasible for large designs.

Software-based self-test (SBST) techniques are used to test processors and processor cores against permanent faults introduced by the manufacturing process or to perform in-field test in safety-critical applications. However, the generation of an SBST program is usually associated with high costs as it requires significant manual effort of a skilled engineer with in-depth knowledge about the processor under test. In this paper, we propose an approach for the automatic generation of SBST programs. First, we detail an automatic test pattern generation (ATPG) framework for the generation of functional test sequences. Second, we describe the extension of this framework with the concept of a validity checker module (VCM), which allows the specification of constraints with regard to the generated sequences. Third, we use the VCM to express typical constraints that exist when SBST is adopted for in-field test. In our experimental results, we evaluate the proposed approach with a microprocessor without interlocked pipeline stages (MIPS)-like microprocessor. The results show that the proposed method is the first approach able to automatically generate SBST programs for both end-of-manufacturing and in-field test whose fault efficiency is superior to those produced by state-of-the-art manual approaches.

Due to the increasing usage of embedded instruments in many electronic devices, new solutions to effectively access these instruments appeared, including the new IEEE 1687 standard. The approach supported by IEEE 1687 allows a flexible access to embedded instruments through the Boundary Scan interface. The IEEE 1687 network includes a set of reconfigurable scan chains. This paper addresses the issue of testing the circuitry implementing them, checking whether any permanent hardware fault exists, affecting either the registers associated to the instruments made accessible by the network, or the configuration structures it embeds (e.g., the multiplexers and the associated flip-flops). The paper proposes an approach, in which the IEEE 1687 network undergoes a sequence of test sessions, each composed of a configuration phase and a test phase. By properly selecting the network configurations to be used, we can guarantee that the method can test any permanent fault possibly affecting the network. We also provide some experimental results gathered on a set of benchmark networks, allowing to practically evaluate the viability of the approach.

Self-Test strategies for testing embedded processors are increasingly diffused. In this paper, we describe a set of self-test techniques tackling dual issue embedded processors. The paper details how to produce test programs suitable to detect stuck-at faults in computational modules belonging to dual issue processors. The proposed technique is aimed at extending single issue test programs; results are illustrated for a 32-bit processor included in an automotive System-on-Chip manufactured by STMicroelectronics and implementing a dual issue strategy with static dispatch of instructions.

Recently, several application areas in the test domain (e.g., burn-in and aging monitoring) started to require suitable input stimuli, able to maximize the switching activity of a certain module for a certain period of time. If the module is part of a processor, this turns into identifying a suitable sequence of instructions, able to maximize the switching activity. This paper proposes a method to attack this problem, and reports some experimental results gathered on a MIPS-like pipelined processor.