Machine Learning-Assisted Modeling of AC Stress-Induced Bias Temperature Instability in Oxide Channel Transistors for 2T Gain Cell eDRAM

Physics-based compact modeling requires substantial work to formulate equations and fine-tune coefficients. In this work, we propose a machine learning (ML)-assisted compact model to capture alternating current (AC)-induced positive bias temperature instability (PBTI) in top-gate indium tungsten oxide (IWO) transistors, demonstrating high accuracy (R2 = 0.94, _ 0.98) across various operating conditions. Aging simulations of 2T gain cell (GC) embedded DRAM (eDRAM) reveal that the write transistor is more vulnerable to AC stress, leading to data hold failures as the number of AC cycles increases. This study highlights the advantages of ML-assisted compact modeling in improving reliability predictions for emerging semiconductor devices.