Designers typically add design margins to semiconductor memories to compensate for aging. However, the aging impact increases with technology downscaling, leading to the need for higher margins. This results into a negative impact on area, yield, performance, and power consumption. As an alternative, mitigation schemes can be developed to reduce such impact. This paper proposes a mitigation scheme for the memory's sense amplifier (SA); the scheme is based on creating a skew in the relative strengths of the SA's cross-coupled inverters during design. The skew is compensated by aging due to unbalanced workloads. As a result, the impact of aging on the SA is reduced. To validate the mitigation scheme, the degradation of the sense amplifier is analyzed for several workloads. The experimental results show that the proposed mitigation scheme reduces the degradation of the sense amplifier's critical figure-of-merit, the offset voltage, with up to 26%.

As technology scales down, the impact of variability due to process variation and aging increases. In order to guarantee an optimal design with a low failure rate, it is crucial to take into account the impact of these sources of variability. Prior work on SRAM reliability has mainly focused on estimating the impact of this variability on the memory cell array, while the peripheral circuitry and the complete memory circuit have received little attention. This study analyzes the impact of aging on a complete 14nm FinFET SRAM circuit. In this analysis, it is investigated how the memory's individual components contribute to the memory's overall degradation. In addition, it is investigated how the application-dependent aging impacts the memory. The results of this work show that, depending on the investigated metric, the peripheral circuitry has a significantly higher contribution to the overall degradation of the memory than the cell array. In addition, the degradation of the memory is shown to be strongly dependent on the application. Overall, the results of this study emphasize that the impact of the peripheral circuitry and the application-dependent aging must be taken into account during design in order to optimally solve SRAM reliability.

Memory designs typically contain design margins to compensate for aging. As aging impact becomes more severe with technology scaling, it is crucial to accurately predict such impact to prevent overestimation or underestimation of the margins. This paper proposes a methodology to accurately and efficiently analyze the impact of aging on the memory's digital logic (e.g., timing circuit and address decoder) while considering realistic workloads extracted from applications. To demonstrate the superiority of the methodology, we analyzed the degradation of the L1 data and instruction caches for an ARM v8-a processor using both our methodology as well as the state-of-the-art methods. The results show that the existing methods may significantly over-or underestimate the impact (e.g., the decoder margin up to 221% and the access time up to 20%) as compared with the proposed scheme. In addition, the results show that in general the instruction cache has the highest degradation. For example, its access time degrades up to 9% and its decoder margin up to 44%.

This paper presents an accurate technique to extensively analyze the impact of time-zero (i.e., global and local variation) and time-dependent (i.e., voltage, temperature, workload, and aging) variation on the offset voltage specification of a memory sense amplifier design using 45 nm predictive technology model (PTM) high performance library. The results show that increasing the supply voltage both for time-zero and time-dependent reduces the offset voltage specification marginally, irrespective of the process corners. In contrast, the offset voltage specification is very sensitive to the temperature and the workload, i.e., the applied voltage patterns. The results also show that a balanced workload results in a significantly lower offset voltage specification. The above results can be used to estimate the required offset voltage accurately for a given lifetime, and operational conditions such as workload, temperature, and voltage; hence, enable the designer to take appropriate measures for a high quality, robust, optimal and reliable design.

This paper proposes an appropriate method to estimate and mitigate the impact of aging on the read path of a high performance SRAM design; it analyzes the impact of the memory cell, and sense amplifier (SA), and their interaction. The method considers different workloads, technology nodes, and inspects both the bit-line swing (BLS) (which reflect the degradation of the cell) and the sensing delay (SD) (which reflects the degradation of the sense amplifier); the voltage swing on the bit lines has a direct impact on the proper functionality of the sense amplifier. The results with respect to the quantification of the aging, show for the considered SRAM read-path design that the cell degradation is marginal as compared to the sense amplifier, while the SD degradation strongly depends on the workload, supply voltage, temperature, and technology nodes (up to 41% degradation). The mitigation schemes, one targeting the cell and one the sense amplifier, confirm the same and show that sense amplifier mitigation (up to 15.2% improvement) is more effective for the SRAM read path than cell mitigation (up to 11.4% improvement).

The CMOS technology scaling faced over the past recent decades severe variability and reliability challenges. One of the major reliability challenges is bias temperature instability (BTI). This paper analyzes the impact of BTI on the sensing delay of standard latch-type sense amplifier (SA), which is one of the critical components of high performance memories; the analysis is done by incorporating the impact of process, voltage, and temperature variations (in order to investigate the severity of the integral impact) and by considering different workloads and four technology nodes (i.e., 45, 32, 22, and 16 nm). The results show the importance of taking the SA degradation into consideration for robust memory design; the SA degradation depends on the application and technology node, and the sensing delay can increase with 184.58% for the worst case conditions at 16 nm. The results also show that the BTI impact for nominal conditions at 16 nm reaches a 12.10% delay increment. On top of that, when extrinsic conditions are considered, the degradation can reach up to 168.45% at 398 K for 16 nm.