Beyond Limits: Machine Learning Driven Reliability Forecasting for Nanoscale ULSI Circuits
Year: 2025
Type: conference-paper
Abstract: This paper presents a machine learning-based predictive model for negative bias temperature instability (NBTI) degradation in nanoscale PMOS transistors. Through extensive HSPICE Synopsys simulations over a decade, encompassing diverse process and bias conditions, the study elucidates the influence of Gate Oxide, Threshold Voltage, Temperature, and Supply Voltage on NBTI. Multiple machine learning algorithms including Linear Regression, Support Vector Regression, Tree Regression, Ensembles of Trees Regression, Gaussian Process Regression, and Neural Networks are trained and validated. Results showcase more than 99% accuracy in forecasting NBTI degradation, facilitating robust design strategies for ULSI digital circuits in industrial 45nm technology nodes.
Authors: Karan Singh, Shruti Kalra
Institution: Jaypee Institute of Information Technology
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4409761751