AI Meets Device Modeling: Transforming Compact Modeling with Machine Learning (semiengineering.com)

Keysight has introduced the ML Optimizer, a machine learning-driven solution designed to transform compact device modeling by overcoming the limitations of traditional gradient-based optimization methods. As semiconductor devices grow more complex—featuring hundreds of interdependent parameters and nonlinear behaviors—classical techniques often struggle with local minima, leading to prolonged, manual, and error-prone model extraction processes. The ML Optimizer employs gradient-free algorithms enhanced with adaptive learning to efficiently navigate these difficult parameter spaces, delivering improved accuracy and faster convergence with minimal manual tuning. This innovation is significant for the AI/ML and semiconductor communities because it automates and scales the parameter extraction workflow, enabling more robust and reliable device simulations amid rapidly evolving materials and architectures. Unlike classical optimizers like Newton-Raphson or Levenberg-Marquardt, the ML Optimizer continuously balances exploration and exploitation strategies, adapting step sizes and directions to optimize high-dimensional, non-convex model spaces more effectively. Integrated into Keysight’s IC-CAP 2025 and MBP 2026 platforms, it accelerates modeling time, reduces human errors, and enhances the fidelity of device simulations crucial for next-generation circuit design. By leveraging machine learning in this context, Keysight’s approach marks a pivotal step toward automating complex physical model extraction, highlighting how AI can address niche yet critical challenges in semiconductor design and validation. As device architectures grow ever more intricate, tools like the ML Optimizer will be instrumental for the AI/ML community to drive innovation in electronic design automation and help meet tight development cycles with higher confidence in model accuracy.