When Simpler Models Win: The Limits of Parameter Sharing Neural Networks in Cross Technology SRAM Analysis

Static Random Access Memory (SRAM) is a critical component in modern electronics, and accurate predictions of its stability are essential for optimizing performance across various technology nodes. In this paper, we propose a neural network (NN) model with parameter sharing designed specifically for cross-node SRAM stability prediction, enabling knowledge transfer between different technology nodes. To ensure the reliability of our model, we use a high-sigma verifier (HSV) to simulate actual statistical values, providing a thorough evaluation of the model performance under varying conditions. While the NN model demonstrates promising results, we find that the machine learning (ML) approach outperforms the NN model in predicting key SRAM metrics, including Access Disturb Margin (ADM), Write Margin (WRM), and $I_{\text {readmin }}$. We provide a detailed comparison between both approaches, highlighting the strengths of the ML model in achieving more accurate predictions, while also demonstrating the potential of the NN model for future improvements in SRAM stability prediction. Such prediction aims to transform design decision making and substantially improves early-stage stability evaluations.