Machine Learning (ML) techniques, especially Artificial Neural Networks (ANNs) have been recognized as a powerful tool to eliminate the fine-tuning process in EM component design and were widely adopted for the modeling, optimization, and/or inverse design of microwave components and circuits. As a proof of concept, we constructed a fully connected DNN to predict the electromagnetic (EM) responses of Schiffman phase shifters given their physical dimensions. 

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