A Combined State Space Formulation/Equivalent Circuit and Neural Network Technique for Modeling of Embedded Passives in Multilayer Printed Circuits - Ding, X.; Xu, J.J.; Zhang, Q.J.; Yagoub, M.C.E. (Applied Computational Electromagnetics Society Journal (ACES), 2022)
In this paper, we present a new approach for modeling the high-frequency effects of embedded passives in multilayer printed circuits, utilizing state space equations or equivalent circuit together with neural network techniques. In this approach, the neural network based model structure is trained using full wave electromagnetic (EM) data. The resulting embedded passive models are accurate and fast, can be used in both frequency/time domain simulators. Examples of embedded resistor and capacitor models emonstrate that the combined model can accurately represent EM behavior in microwave/RF circuit design. In high-level circuit design, we applied our combined EM based neural models for signal integrity analysis and design of multilayer circuit to illustrate that the geometrical parameters can be continuously adjusted by using neural network techniques. Optimization and Monte-Carlo analysis are performed showing that the combined models can be efficiently used in place of computationally intensive EM models of embedded passives to speed up circuit design.