Machine Learning‐Driven Extraction of Hybrid Compact Models Integrating Neural Networks and Berkeley Short‐Channel Insulated‐Gate Field‐Effect Transistor Model‐Common Multigate for Multidevice Applications

Conventional techniques for extracting physics‐based model parameters are inherently slow processes and often yield less accurate model parameters because of the inflexibility of physical equations. This study presents a novel machine learning–based method to accelerate and enhance the accuracy of compact model generation for multiple devices simultaneously. By integrating a Berkeley short‐channel Insulated‐Gate Field‐Effect Transistor (IGFET) model‐common multigate (BSIM) model with an error‐correction neural network, the proposed approach refines predictions for critical electrical behaviors such as drain current and gate capacitance. Extraction networks dynamically optimize parameter sets for both models, eliminating manual tuning and reducing the need for separate training for each device. The method was validated using TCAD‐simulated 3 nm nanosheet field‐effect transistors devices, achieving a mean absolute percentage error of 1.8% for drain current, 2.8% for transconductance, 8.5% for output conductance, and 0.4% for capacitances. Compared with the BSIM model alone, error reductions of 75, 70, 39, 85, and 81%, respectively, were achieved. This approach showed significant error reductions compared to the BSIM model alone and demonstrated robust performance across devices with variations, proving its effectiveness for large‐scale applications.