Neuro-Space Mapping technique for nonlinear device modeling and large signal simulation

Abstract

A new Neuro-Space Mapping (Neuro-SM) approach is presented enabling the space mapping (SM) concept to be applied to nonlinear device modeling and large signal circuit simulation. Suppose that an existing device model (namely, the coarse model) cannot match the actual device behavior (namely, the fine model). Using the proposed technique, the voltage and current signals between the coarse and the fine device models are mapped by a neural network. This mapping automatically modifies the behavior of the coarse model such that the mapped model accurately matches the actual device behavior. New training methods for such mapping neural networks are proposed. Examples of SiGe HBT and GaAs MESFET modeling and use of the models in harmonic balance simulation demonstrate that Neuro-SM is a systematic method to allow us to exceed the present capabilities of the existing device models.