Training Free Parameter Extraction for Compact Device Models using Sequential Bayesian Optimization

This work presents a computationally efficient approach to extract the compact model parameters without extensive training requirements. Bayesian optimization is employed in multiple stages to optimize different model parameters. The methodology is demonstrated on Virtual Source model (MVS 2.0), extended for Nanosheet FET and MoS <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> based 2DFET. Optimization function based on I-V characteristics slope, on and off currents ensures optimum fitting of global as well as local model parameters for diverse devices.