Accelerating Compact Model Parameter Extraction with the Machine Learning Optimizer
应用文章
As semiconductor devices evolve toward more complex architectures, compact models require hundreds of parameters and exhibit strong nonlinearities. Traditional extraction methods, struggle with local minima, noise sensitivity, and heavy dependence on user expertise—making the process lengthy and difficult to maintain.
The Machine Learning (ML) Optimizer offers a smarter, automated solution. It dynamically learns from prior evaluations to efficiently balance global exploration and local refinement, reducing flow complexity and improving convergence robustness.
This application note introduces the ML Optimizer’s key capabilities and demonstrates, through two practical examples, how it accelerates parameter extraction, enhances fitting accuracy, and enables scalable, fully automated modeling across different compact models and device technologies.