As semiconductor technologies advance toward FinFETs, GAAFETs, and wide bandgap devices, device modeling has become increasingly complex. Traditional parameter extraction approaches often require numerous iterations and manual tuning, making the process time-consuming and highly dependent on user expertise. To address these challenges, artificial intelligence (AI) and machine learning (ML) are emerging as powerful enablers for next-generation device modeling. In this webinar, we’ll show how the Machine Learning Optimizer in Device Modeling MBP dramatically improves modeling efficiency by automating and simplifying complex workflows. By transforming traditional multi-stage workflow into a few streamlined steps, the ML Optimizer reduces complexity, minimizes manual effort, and consistently delivers a globally optimal solution. Once created, an ML Optimizer flow can be easily reused and adapted for other device types or technologies, accelerating model development for new nodes . By attending this webinar, participants will see practical demonstrations of ML Optimizer applied to industry-standard compact models and learn how to build efficient, automated flows using MBP’s Python environment.
