Rapid Multiobjective Optimization Strategy of TSV-Based 3-D Inductor Using Vector Fitting and Evolutionary Algorithm
Haoxiang Huang, Fengjuan Wang, Xiangkun Yin, Minghua Zhao, Ming Xu, Ningmei Yu, Yuan Yang, Cheng Shi
Abstract
In this work, an effective and efficient optimization strategy for 3-D inductors based on through-silicon via (TSV) is proposed. This strategy is based on the nondominated sorting genetic algorithm-II (NSGA-II) and introduces the artificial neural network (ANN) and vector fitting (VF) for surrogate modeling. It reduces time-consuming multitimes electromagnetic (EM) simulations while obtaining Pareto sets. The effectiveness of this optimization strategy can be demonstrated by the presentation of the cases. The accuracy of the strategy is corroborated by comparing the data of solution sets with the EM simulation results. Meanwhile, the time consumed by the optimization process in this strategy is only about 30 min after ANN training.