Machine Learning for SRAM Stability Analysis

SRAM stability is a critical challenge in technology scaling due to process variations. In this paper, we introduce a cutting-edge approach leveraging machine learning based on device and bitcell simulation to predict SRAM behavior in high sigma local and global variations. Our focus includes both high-density (HDC) and Low Voltage Cell (LVC) analysis, revealing the Extreme Gradient Boosting Regressor (XGBR) as the top performer for both. This research demonstrates the superior accuracy of the XGBR regressor in predicting key SRAM metrics, such as Access Disturb Margin (ADM), Write Margin (WRM), and I
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, offering a compelling alternative to traditional statistical simulations. The purpose of such prediction is to revolutionize the design process and speed up designers’ decisionmaking.