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.@en

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.@en

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.@en

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.

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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.

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Resistive random access memory (RRAM) is a promising emerging memory technology that offers dense, non-volatile memories that do not consume any static power. Furthermore, RRAMdevices can be written and read out in nanoseconds, and it is possible to use them to performcomputation-in-memory (CIM). These benefits make this technology a potential replacement for Flash or even dynamic random access memory (DRAM). This is also clearly seen by the community; both universities and companies are prototyping RRAMs, and there are already some commercial RRAMs available. In order to deliver high-quality products, the RRAMs need to be tested properly so that a manufacturer can guarantee the quality. This dissertation focuses on test development for RRAMs. Traditionally, production defects in memories, such as DRAM, are modeled as linear resistors in or between two nodes of the circuit. In literature, many researchers have applied a similar approach for RRAMs. However, we demonstrate that this method of modeling defects is inappropriate, because the models fail to describe the defective behavior of the RRAM device. Instead, those models describe defects in the interconnections that surround the RRAM device. To overcome this, we propose the Device-Aware Test (DAT) approach that consists of three steps. First, the approach models the actual physics of defective devices and thus leads to realistic defectmodels. Second, the defect models are used to performaccurate faultmodeling and analysis. Third, the results from this step are used to develop high-quality RRAM tests. We do this by first characterizing the defect. We analyze the complete production process of a RRAM. During this analysis, we identify what can go wrong in every step, and in what kind of defects this may result. All identified defects need to be properly modeled, so that a high-quality test can be developed.Next,we analyze howit affects the performance of a defect-free device, and incorporate the resulting defective behavior in a compact defect model. This model is calibrated and accurately describes the effects of the defect. Second, we apply this defect model in a RRAM circuit to perform fault modeling and analysis. We systematically define the complete space of all faults that could occur, and then apply an analysis methodology to validate which faults actually occur in the circuit. Third, we develop a test for the validated faults. This test only needs to detect faults that are actually sensitized, and thus is shorter than generic tests, while it also has a better fault coverage. We apply the DAT approach to RRAM forming defects and RRAM intermittent undefined state faults. The results show that these two defect models sensitize different faults than the traditional defect models do. Since the DAT defect models describe the actual physics of the defects, we can conclude that the traditional approach will lead to lowquality tests that generate test escapes and reduce the production yield. Furthermore, we demonstrate that the faults cannot easily be detected by existing test algorithms, and that special tests need to be developed to detect them. We also apply the DAT approach to a RRAM-based computation-in-memory (CIM) architecture to develop a test for it. We shows that a CIM device needs to be tested both in its memory and computation configuration, as there are unique faults in both configurations. We define the complete fault space for CIM faults and validate it using the DAT approach. Subsequently, we develop a test that detects the faults in both configurations. Furthermore, we study how process, voltage and temperature variations affect the performance of the CIM architecture. We demonstrate that certain operations are more susceptible to these variations than other ones. @en

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.

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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.@en

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@en

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.@en

Manufacturing defects in FinFET SRAMs can cause hard-to-detect faults such as Undefined State Faults (USFs). Detection of USFs is not trivial, as they may not lead to incorrect functionality. Nevertheless, undetected USFs may have a severe impact on the memory's quality: they can cause random read outputs, which might lead to test escapes and no-trouble-found devices later when the device is already in the field, as well as compromise the circuit's quality by reducing the memory cell's Static Noise Margin (SNM). Therefore, the detection of USF is critical. This paper proposes a test solution to improve the detection of USFs in FinFET SRAMs. To achieve this, we first analyze the impact of USFs on the cell's SNM and bitline swing during read operations. Then, we perform an experimental study of stress conditions' (SCs) impact on sensitizing and detecting USFs. Finally, we propose a dedicated Design-For-Testability (DFT) scheme for FinFET SRAMs to detect such faults. This scheme introduces a small area overhead while significantly improving USF detection. Hence, using the proposed DFT leads to fewer test escapes and higher-quality FinFET SRAMs.@en

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.

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Today's computer architectures and semiconductor technologies are facing major challenges making them incapable to deliver the required features (such as computer efficiency) for emerging applications. Alternative architectures are being under investigation in order to continue deliver sustainable benefits for the foreseeable future society at affordable cost. These architectures are not only changing the traditional computing paradigm (e.g., in terms of programming models, compilers, circuit design), but also setting up new challenges and directions on the way these architectures should be tested to guarantee the required quality and reliability levels. This paper highlights the major open questions regarding test and reliability of three emerging computing paradigms being approximate computing, computation-in-memory and neuromorphic computing.@en

Resistive RAM (RRAM) is one of the emerging nonvolatile 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.@en