Performance modeling of analog circuits via neural networks: the design process view

This paper presents a novel framework for modeling the performance of analog circuits and a methodology for constructing the corresponding models. The methodology uses simulator data to iteratively train neural networks in order to produce very accurate multivariate nonlinear models, which can be instantaneously evaluated. Results are presented for circuits at various levels of abstraction.