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

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 neural network DMF modeling built using smooth maximum unit function log-cosh function. The parasitic capacitive effect DMFs quickly accurately extracted though this model, effective permittivity can be obtained subsequently. An containing filling densities analyzed proposed method compared simulation entire structures. results validate accuracy efficiency method.