DOI: 10.25549/usctheses-c40-379755
title: Yield estimation, design, and testing of SRAMs under correlated process variations and bias temperature instability (BTI) aging

description: Process variations and aging are two of the major sources of the degradation of yield and circuit reliability. In our research we focus on the impact of process variations and aging on static random-access memory (SRAM). SRAMs are widely used in modern processors typically as caches. They are particularly sensitive to process variations and aging due to their high densities and small transistor sizes. Hence, yield and reliability of modern processors are highly dependent on the yield and reliability of SRAMs. ❧ In the first part of our research, we study yield estimation and design of SRAMs under correlated process variations. Process variations have been shown to be spatially correlated in nature. However, most of the existing research on SRAMs has ignored such spatial correlation of process variations, i.e., process variations are mostly assumed to be independent between different transistors in order to simplify yields estimation of SRAMs. In our research we show that such simplification greatly overestimates the impact of process variations and hence underestimates the yields of SRAM arrays. To better understand the impact of spatial correlation on SRAMs, we propose the first method to accurately and efficiently estimate yield of SRAM arrays under correlated process variations. Furthermore, using this newly-developed yield estimation method, we comprehensively investigate the  implications of correlation on SRAM design. We quantitatively characterize the clustering of SRAM failures caused by correlated variations and propose novel design methods to maximize yield-per-area of SRAM arrays when correlation is taken into consideration. ❧ In the second part of our research, we study predictive testing of SRAMs against bias temperature instability (BTI) aging. BTI is one of the most significant sources of transistor aging. It degrades SRAM stability, causing in-field failures of SRAMs. In our research we develop efficient methods for predictive testing for aging, i.e., methods that can be used right after fabrication to identify and eliminate chips that will fail during the desired lifetimes under future aging. Specifically, we propose two novel post-fabrication testing methods, wordline overdriving test (WODT) and stress-test-recovery (STR) test, to effectively detect the SRAM cells that are likely to fail under future aging. These methods are shown to be dramatically more effective than the commonly used low-VDD test for SRAMs. ❧ Furthermore, the magnitude of aging of each SRAM cell is dependent on the workload of the SRAM cell over lifetime. Since the exact workload is not known deterministically before actual usage, the amount of aging of SRAM cells is also random, hence cannot be predicted precisely. However, the statistical distribution of workload of SRAMs in the cache over lifetime can be estimated by simulating typical benchmarks. In our research we develop a systematic and effective method to take advantage of this statistical distribution of the workload to analytically find the optimal testing conditions for the proposed predictive testing methods. ❧ The impact of inherent randomness of BTI aging on predictive testing is also studied in our research. Researchers have shown that BTI aging is inherently random due to its physical mechanism. We find that such randomness can significantly degrade the effectiveness of the proposed predictive testing methods. In order to overcome this challenge, we propose to combine the online predictive tests with post-fabrication predictive tests. In the proposed method, the spare resources, such as spare rows and columns, are not configured for repair immediately after fabrication. Instead, they are configured to repair the SRAM failures that are predicted by the online predictive tests in order to minimize the impact of randomness of aging. Simulation results show that this testing and repair method can dramatically improve the effectiveness of predictive testing under random aging. In our research we address several of the key challenges of online predictive testing, and outline future research to make this testing method fully applicable in practice.