Surrogate Modeling with Complex-valued Neural Nets and its Application to Design of sub-THz Patch Antenna-in-Package
Surrogate Modeling with Complex-valued Neural Nets and its Application to Design of sub-THz Patch Antenna-in-Package
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DOI：
10.1109/ims37964.2023.10187990
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
O. Akinwande;Osama Waqar Bhatti;Kai-Qi Huang;Xingchen Li;Madhavan Swaminathan
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文献类型：
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作者：
O. Akinwande;Osama Waqar Bhatti;Kai-Qi Huang;Xingchen Li;Madhavan Swaminathan
本文提出了一种复值神经网络正演和逆演的代理模型。复杂域提供了更高的功能和更好的表示。为此，我们提出了一个深层复杂密集网络（DNet），通过引入由支持复杂操作的全连接层构建的复杂密集块。我们进一步提出了一个逆优化目标，使建模误差最小化，同时优化设计空间参数以达到目标规格。我们将提出的方法应用于工作在140 GHz频段的亚太赫兹贴片天线的封装设计。
In this paper, we propose a surrogate model for both forward and inverse modeling with complex-valued neural networks. The complex domain offers the benefits of higher functionality and better representation. To that end, we propose a deep complex dense network (ℂDNet) by introducing complex dense blocks built with fully-connected layers that support complex operations. We further propose an inverse optimization objective that minimizes the modeling error while optimizing the design space parameters that achieve the target specifications. We apply our proposed approach for the design of a sub-THz patch antenna-in-package operating at 140 GHz frequency band.