Open AccessA Deep-Learning Approach for Wideband Design of 3D TSV-Based Inductors
Author(s) -
Xiangliang Li,
Peng Zhao,
Shichang Chen,
Kuiwen Xu,
Gaofeng Wang
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3230986
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
A high-efficient wideband through-silicon vias (TSVs) modeling method based on deep learning is proposed, and a compact three-dimensional (3D) spiral inductor is designed using the proposed method. By comparing different activation functions and loss functions, an adaptive deep neural network (DNN) based on Gaussian Error Linear Unit (GELU) and Huber functions for constructing parameterized TSV models is proposed. The model has much higher accuracy and better robustness than commonly used circuit equivalent models over a wide range of bandwidths. Moreover, a compact 3D spiral inductor using ground TSV is designed based on the DNN model. This 3D inductor greatly reduces the inductor area compared to planar inductors and has weak crosstalk between TSV pairs. The designed inductor is simulated by direct electromagnetic calculation to verify the proposed method and design.