Piecewise-linear modeling of analog circuits based on model extraction from trained neural networks

This paper presents a new technique for automatically creating analog circuit models. The method extracts piecewise linear models from trained neural networks. A model is a set of linear dependencies between circuit performance and design parameters. The paper illustrates the technique for an OTA circuit for which models for gain and bandwidth are generated. As experiments show, the obtained models have simple form that accurately fits the sampled points. These models are useful for fast simulation of systems with nonlinear behavior and performance.