Extrapolation Techniques for Neural-based Nonlinear Electronic Modeling and Circuit Simulation

Abstract

Neural network based techniques are useful in representing the behavior of electronic devices for nonlinear circuit simulation. As new technologies evolve, new behavior need to be captured for modeling and simulation. Often, measurement data from the devices are used to train a neural model to capture these behaviors. However, during nonlinear simulation, the nonlinear equations derived from the electronic circuits need to be solved iteratively, and the iterative variables may explore outside the training data range, potentially causing convergence failure. This paper addresses such problem by describing an extrapolation technique that extrapolate the information smoothly beyond the training data range. To guarantee convergence of nonlinear simulation, the extrapolated model is made maximumly smooth while satisfying diverse tendencies of the model in multi-dimensional parameter space. The technique is demonstrated by a high-electron-mobility transistor modeling example and its use in harmonic-balance simulation, showing better convergence of nonlinear circuit simulation using extrapolated neural models over existing neural modeling methods.