While Resistive RRAM (RRAM) provides appealing features for artificial neural networks (NN) such as low power operation and high density, its conductance variation can pose significant challenges for synaptic weight storage. This paper reports an experimental evaluation of the conductance variations of manufactured RRAMs memory cells at the memory array level. Variability is evaluated with respect to the RRAM low resistance state (LRS) and high resistance state (HRS) conductance ratio. This ratio is selected as the parameter of interest as it guarantees the proper operation of the RRAM: the larger the ratio, the more reliable and robust the RRAM cell is in storing and retrieving data. The measurement results show that conductance ratio is significantly influenced by variability. Using these findings, the performance of an artificial neural network that uses individual RRAM cells for synaptic weight storage is evaluated in relation to conductance variability. It is shown that RRAM variability can heavily affect the network behavior, resulting in a substantial decrease in the classification accuracy during inference.

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 (H_ext) 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 H_ext-assisted tests is presented and implemented for all known conventional and unique defects. The paper demonstrates that the H_ext-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.

Resistive Random-Access Memories (ReRAMs) represent a promising candidate to complement and/or replace CMOS-based memories used in several emerging applications. Despite all the advantages of using these novel memories, mainly due to the memristive device's CMOS manufacturing process compatibility, zero standby power consumption, as well as, high scalability and density, the use of them in real applications depends on being able to guarantee their quality after manufacturing. As observed in CMOS-based memories, ReRAMs are also susceptible to manufacturing deviations, defects, and process variations, that can cause faulty behaviors different from the ones observed in CMOS technology, increasing not only the manufacturing test complexity but also the time required to perform the test. In this context, this paper proposes to study the use of temperature to facilitate fault propagation in ReRAMs, reducing the required test time. A case study composed of a 3x3 word-based ReRAM with peripheral circuitry implemented based on a 130 nm Predictive Technology Model (PTM) library was adopted. During the proposed study, a total of 17 defects were injected in different positions of the ReRAM cell, and their respective faulty behavior was classified into traditional and unique faults, considering three temperatures (25, 100, and -40°C). The obtained results show that the temperature can, depending on the position of the defect, facilitate fault propagation, which reduces the time required for performing manufacturing testing.

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

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.

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

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.

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 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.

While Resistive RRAM (RRAM) offers attractive features for artificial neural networks (NN) such as low power operation and high-density, its conductance variation can pose significant challenges when the storage of synaptic weights is concerned. This paper reports an experimental evaluation of the conductance variations of manufactured RRAMs at the memory array level. Working at the memory array level allows to catch cycle-to-cycle (C2C) as well as device-to-device (D2D) variability and, hence, to propose a realistic evaluation of the conductance variation. Variability is evaluated with respect to the RRAM low resistance state (LRS) and high resistance state (HRS) conductance ratio. This ratio is selected as the parameter of interest as it guarantees the proper operation of the RRAM: the larger the ratio, the more reliable and robust the RRAM cell is in storing and retrieving data. The measurement results show that the conductance ratio is heavily affected by variability. Large spatial and temporal variations are reported, making challenging RRAM-based analog weight storage.

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.

The Advanced Encryption Standard (AES) is widely recognized as a robust cryptographic algorithm utilized to protect data integrity and confidentiality. When it comes to lightweight implementations of the algorithm, the literature mainly emphasizes area and power optimization, often overlooking considerations related to performance and security. This paper evaluates two of our previously proposed lightweight AES implementations using both profiled and non-profiled attacks. One is an unprotected implementation, and the other one is a protected version using Domain-Oriented Masking (DOM). The findings of this study indicate that the inclusion of DOM in the design enhances its resistance to attacks at the cost of doubling the area.

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.

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

According to the World Economic Forum, cyberattacks are considered as one of the most important sources of risk to companies and institutions worldwide. Attacks can target the network, software, and/or hardware. Over the years, much knowledge has been developed to understand and mitigate cyberattacks. However, new threats have appeared in recent years regarding software attacks that exploit hardware vulnerabilities. This article defines these attacks as architectural attacks. Today, both industry and academia have only limited comprehension of architectural attacks, which represents a critical issue for the design of future systems. To this end, this work proposes a new taxonomy, a new attack model, and a complete survey of existing architectural attacks. As a result, it provides the tools to understand architectural attacks in more depth and to start building improved designs and protection mechanisms.

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.

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

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.