Inverse Design of Bonding Wire Array Based on Multifidelity Data-Enabled Neural Networks
Jingwei Zhang, Zhun Wei, Enze Zhu, Weiqiang Wu, Yanning Chen, Kai Kang, Hao Xie, Dongyan Zhao, Wen‐Yan Yin
Abstract
Bonding wire arrays (BWAs) play an important role in multichip systems. In particular, the inductance, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> value, and loss of BWA are closely related to the performance of the chip systems. However, in industrial applications, full-wave numerical design approaches bring data burden and high computational costs, particularly for large-scale BWA. To alleviate the problem, we propose the multifidelity data-enabled neural networks for inversely designing BWA, which consist of an inversion network and a residual network. Specifically, we first introduce an approximate analytical model of BWA and perform low-fidelity data collection with the analytical model to train the inversion network. Subsequently, a small set of high-fidelity data is used to train the residual network, which is targeted to learn the residual between the analytical solutions and high-fidelity results. During the testing process, we cascade the two trained networks. Intensive numerical results are used to test the proposed method, where the average testing accuracy reaches 94.30% with 5000 tests. To demonstrate the effectiveness of the proposed method, the inversely designed BWA is fabricated and measured experimentally, where the measured results satisfy the performance demands. Furthermore, a power amplifier (PA) using bonding wires as matching inductors is designed, fabricated, and measured by using the proposed method.