A Novel Physics Aware ANN-Based Framework for BSIM-CMG Model Parameter Extraction

In this article, we present a novel deep learning (DL) framework that fully automates the parameter extraction process for the BSIM-CMG unified model for advanced semiconductor devices. The framework seamlessly integrates with the BSIM-CMG model, making it applicable to diverse advanced devices such as GAA nanosheets, nanowire FETs, and FinFETs. Unlike existing approach involving DL for parameter extraction, the proposed framework combines physics-driven parameter initialization and data-driven DL enhancing the computational efficiency and making it easy to implement. It leverages the BSIM-CMG model's versatility for initial parameter estimation, the efficiency of DL algorithms for model parameter prediction, and the adaptability to various device geometries and configuration. The framework has been successfully validated with extensive numerical simulations and experimental data from 14-nm FinFET device with varying channel widths, 12-nm nanosheet, and 24-nm nanowire FET.