论文标题
深度学习辅助端到端的MM波无源网络具有3D EM结构：基于变压器的匹配网络的研究
Deep Learning Assisted End-to-End Synthesis of mm-Wave Passive Networks with 3D EM Structures: A Study on A Transformer-Based Matching Network
论文作者
论文摘要
本文提出了一种深度学习的辅助综合方法，用于直接端到端使用3D EM结构的RF/mm-Wave被动匹配网络。与先前从目标电路组件值和目标拓扑合成EM结构的方法不同，我们所提出的方法可以直接合成被动网络的直接综合，从所需的性能值作为输入中的网络拓扑。我们在基于变压器的阻抗匹配网络上展示了提出的合成神经网络（NN）模型。通过利用参数共享，综合NN模型成功地从输入阻抗和负载电容器中提取了相关特征，并在45nm SOI过程中预测了变压器3D EM几何形状，该过程将与标准的50 $ω$载荷与目标输入阻抗相匹配，同时吸收两个负载载体。作为概念验证，合成了几种示例变压器几何形状，并在ANSYS HFSS中进行了验证，以提供所需的输入阻抗。
This paper presents a deep learning assisted synthesis approach for direct end-to-end generation of RF/mm-wave passive matching network with 3D EM structures. Different from prior approaches that synthesize EM structures from target circuit component values and target topologies, our proposed approach achieves the direct synthesis of the passive network given the network topology from desired performance values as input. We showcase the proposed synthesis Neural Network (NN) model on an on-chip 1:1 transformer-based impedance matching network. By leveraging parameter sharing, the synthesis NN model successfully extracts relevant features from the input impedance and load capacitors, and predict the transformer 3D EM geometry in a 45nm SOI process that will match the standard 50$Ω$ load to the target input impedance while absorbing the two loading capacitors. As a proof-of-concept, several example transformer geometries were synthesized, and verified in Ansys HFSS to provide the desired input impedance.
