Wideband Schiffman Phase Shifters Designed with Deep Neural Networks

S. An, B. Zheng, H. Tang, H. Li, L. Zhou, Y. Dong, M. Haerinia, Houyu Zhang
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引用次数: 1

Abstract

Phase shifters play an important role in beam scanning phased arrays, modulators and communication systems. Ideal phase shifters should provide flat phase shift over wide operating frequency band with low insertion and return loss. Schiffman phase shifters are compact in size, easy to fabricate using Print Circuit Board (PCB) technology, while still provide accurate phase shift in relatively wide bandwidth (usually $\gt 50$%), making them stand out from various wideband phase shifter designs. However, due to the approximations being used during the design and the unquantifiable influences of the chamfered entries, fringing effects and parasite inductances of the narrow links between coupled lines, it's hard to analytically calculate the electromagnetic (EM) response of a Schiffman phase shifter given the dimensions. As a result, time-consuming fine-tuning is still required during the design process. In this paper, a novel DNN approach is introduced for the fast inverse design of wideband Schiffman phase shifters. For the first time, a predicting neural network that is capable of simultaneously modeling the phase shift, insertion loss and return loss of Schiffman phase shifter structures over a relative wide spectrum (133%) has been demonstrated. Based on the highly accurate forward predicting network, a tandem inverse design network was also constructed for the fast inverse designs of Schiffman phase shifter with arbitrary phase shift and bandwidth targets. Different from traditional design approaches, the well-trained inverse design network generates design parameters in milliseconds, with no further EM simulation needed. Several Schiffman phase shifters with 60% and 40% fractional bandwidth were designed, fabricated and tested to verify the efficacy of the proposed approach. This DNN-enabled method validates the feasibility of on-demand wideband phase shifter designs, which can be easily generalized to other EM problems, including but not limited to antenna design, microwave circuit design and EM compatibility problems.
基于深度神经网络的宽带希夫曼移相器设计
移相器在波束扫描相控阵、调制器和通信系统中起着重要的作用。理想的移相器应该在较宽的工作频带内提供平坦的移相,并且具有较低的插入损耗和回波损耗。希夫曼移相器尺寸紧凑,易于使用印刷电路板(PCB)技术制造,同时仍然在相对较宽的带宽(通常为50 %)下提供准确的相移,使其从各种宽带移相器设计中脱颖而出。然而,由于在设计过程中使用了近似方法,并且耦合线之间窄链路的倒角入口、边缘效应和寄生电感的影响无法量化,因此在给定尺寸的情况下,很难解析计算希夫曼移相器的电磁响应。因此,在设计过程中仍然需要进行耗时的微调。本文提出了一种基于深度神经网络的宽带希夫曼移相器快速反设计方法。该研究首次证明了一种预测神经网络能够在相对宽的频谱(133%)范围内同时对希夫曼移相器结构的相移、插入损耗和回波损耗进行建模。在高精度正演预测网络的基础上,构建了具有任意相移和带宽目标的希夫曼移相器串联反设计网络,实现了希夫曼移相器快速反设计。与传统的设计方法不同,训练有素的反设计网络在毫秒内生成设计参数,无需进一步的电磁仿真。设计、制作和测试了几种分数带宽分别为60%和40%的希夫曼移相器,以验证该方法的有效性。这种支持dnn的方法验证了按需宽带移相器设计的可行性,可以很容易地推广到其他电磁问题,包括但不限于天线设计、微波电路设计和电磁兼容问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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