Benchmarks for SPICE Modeling and Parameter Extraction Based on AI/ML

Abstract

Over the past decades, the number of submitted articles that use numerical approaches for SPICE models or for characterization (extraction) of parameters of existing SPICE models has grown significantly. Many of those articles rely on synthetic data, generated either from technology computer-aided design (TCAD) or from physical SPICE model simulations; most do not model/fit measured data. Furthermore, those articles do not evaluate the physical correctness, smoothness/monotonicity, or asymptotic correctness of the approach they propose. That is sufficient for initial evaluation of proposed techniques. However, it does not prove that they are “industrial strength.” This article presents benchmarks/guidelines for the proposed artificial intelligence (AI)/machine learning (ML) SPICE modeling and characterization techniques to try to help them become practical and useful.