A Deep Learning Approach for Efficient Electromagnetic Analysis of On-Chip Inductor with Dummy Metal Fillings

Abstract

A deep learning approach for the efficient electromagnetic analysis of an on-chip inductor with dummy metal fillings (DMFs) is proposed. By comparing different activation functions and loss functions, a deep neural network for DMF modeling is built using a smooth maximum unit activation function and log-cosh loss function. The parasitic capacitive effect of DMFs is quickly and accurately extracted though this model, and the effective permittivity can be obtained subsequently. An on-chip inductor containing DMFs with different filling densities is analyzed using this proposed method and compared with the electromagnetic simulation of entire structures. The results validate the accuracy and efficiency of this proposed method.