Improving Compact Model Extraction with ML Optimizer
アプリケーションノート
Compact model extraction is challenged by variations in device physics, parameter coupling, and fitting targets, often requiring complex, manual workflows. This application note presents the ML Optimizer in MBP as an adaptive framework that streamlines extraction while maintaining flexibility.
Five examples — BSIM4, BSIM-BULK, PSP, Gummel-Poon, and VBIC — demonstrate its effectiveness across diverse models. The ML Optimizer reduces workflow complexity, shortens extraction time, and improves robustness by minimizing sensitivity to initial conditions. It also reduces manual tuning and enables consistent results, while supporting reuse of extraction flows across related technologies.