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

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

Memristive devices have become promising candidates to complement and/or replace the CMOS technology, due to their CMOS manufacturing process compatibility, zero standby power consumption, high scalability, as well as their capability to implement high-density memories and new computing paradigms. Despite these advantages, memristive devices are also susceptible to manufacturing defects that may cause different faulty behaviors not observed in CMOS technology, significantly increasing the manufacturing test complexity. This work proposes a Design-for-Testability (DfT) strategy based on the introduction of a on-chip sensor that measures the current consumption of Resistive Random Access Memories (RRAMs) cells to provide the detection of unique faults. The new On-Chip Sensor (ON_CS) was validated using a case study 3×3 RRAM cell array with peripheral circuitry implemented based on a 130 nm Predictive Technology Model (PTM) library. Experimental results show that the proposed DfT strategy is able to detect not only traditional faults, but also unique faults that can affect RRAM cells. Finally, this paper proposes an DfT strategy that can detect unique faults with an unique operation and can be used during the normal operation of a RRAM.@en

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

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

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

Over the last fifty years Complementary Metal Oxide Semiconductor (CMOS) technology has been scaled down, making the design of high-performance applications possible. However, there is a growing concern that device scaling will become infeasible below a certain feature size. In parallel, emerging applications present high demands regarding storage and computing capability, combined with challenging constraints. In this scenario, memristive devices have become promising candidates to replace or complement CMOS technology due to their CMOS manufacturing process compatibility, zero standby power consumption as well as high scalability and density. Despite these advantages, the implementation of high-density memories based on memristive devices poses some challenges related manufacturing process variation and consequently, to their reliability during lifetime. This paper investigates the impact of manufacturing process variation on Resistive Random Access Memories (RRAMs). In more detail, an evaluation of the RRAM's functionality when considering different levels of manufacturing process variation is performed. The obtained results show that different parameters can degrade the functionality of the RRAM cell as well as that there is a relation between the performed operating sequence and the tolerated percentage of variability. Finally, it is important to mention that understanding how process variation impacts the functionality of RRAM cells is considered essential to guarantee their reliability during lifetime, also allowing to optimize manufacturing processes.@en

Over the last fifty years Complementary Metal Oxide Semiconductor (CMOS) technology has been scaled down, making the design of high-performance applications possible. However, there is a growing concern that device scaling will become infeasible below a certain feature size. In parallel, emerging applications present high demands regarding storage and computing capability, combined with challenging constraints. In this scenario, memristive devices have become promising candidates to replace or complement CMOS technology due to their CMOS manufacturing process compatibility, zero standby power consumption as well as high scalability and density. Despite these advantages, the implementation of high-density memories based on memristive devices poses some challenges related manufacturing process variation and consequently, to their reliability during lifetime. This paper investigates the impact of manufacturing process variation on Resistive Random Access Memories (RRAMs). In more detail, an evaluation of the RRAM's functionality when considering different levels of manufacturing process variation is performed. The obtained results show that different parameters can degrade the functionality of the RRAM cell as well as that there is a relation between the performed operating sequence and the tolerated percentage of variability. Finally, it is important to mention that understanding how process variation impacts the functionality of RRAM cells is considered essential to guarantee their reliability during lifetime, also allowing to optimize manufacturing processes.@en

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