Cross Technology Prediction for SRAM Stability Analysis

SRAM stability presents a significant challenge in technology scaling due to process variations. This paper introduces CrossNodeML, a machine learning-based approach that leverages device and bitcell simulations to predict SRAM behavior across new technology nodes using data from previous nodes. Our model is evaluated with three critical SRAM metrics: Access Disturb Margin (ADM), Write Margin (WRM), and I<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">readmin</inf>. Comparisons with high-sigma verifier tool simulations demonstrate the accuracy of our predictions, significantly reducing the need for full statistical simulations in new nodes. This methodology revolutionizes design decisions and significantly enhances early stability assessments.