Parameter-free Neural Field-based Optimal Design of Nonuniform Transmission Lines

This paper presents a novel method for resolution free, free-form shape optimization for nonuniform transmission lines using artificial neural networks. We use a low dimensional representation of the geometries by learning a neural field embedding with a contrastive loss function to group similar geometries. A second neural network predicts the scattering parameters from the encoded geometry for a given frequency point, that are used to define the optimization objective in the frequency domain. The whole pipeline is fully-differentiable enabling the use of fast gradient based optimization methods. The proposed model architecture shows promising results for the simple test case of optimizing a transmission line taper. The method is fast, stable and flexible to apply to different geometries with different constraints and requirements.