Recent advances in neural based time domain EM modeling and simulation

In this paper, the recent neural network (NN) approaches to time domain electromagnetic (EM)-based modeling are summarized. Fast and accurate passive EM models can be created using three recent methods, i.e., (equivalent circuit and neural network) EC-NN, (state space equation and neural network) SSE-NN and (equivalent circuit, state space equation and neural network) EC-SSE-NN. Those methods are based on combined equivalent circuit and/or state space theory. Each of the combined modeling techniques has its own usage depending on the availability of the equivalent circuit and user-desired accuracy. In order to develop a nonlinear transient model to be used together with passive components in time domain EM-based simulation, the adjoint dynamic neural network (ADNN)-based modeling technique can be utilized. Through accurate and fast time domain EM-based neural models of passive/active components, we enable consideration of EM effects in high-frequency and high-speed computer-aided design (CAD), including component's geometrical/physical parameters as optimization variables. Examples of EM modeling of embedded passives and their use in time domain EM-based simulation and design are presented