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A Novel Deep Neural Network Methodology for Fast Scattering-Parameter Extraction of Discretized Structures in Three-Dimensional Space
  • Stephen Newberry ,
  • Ata Zadehgol
Stephen Newberry
University of Idaho

Corresponding Author:[email protected]

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Ata Zadehgol
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Design and analysis for electromagnetic compatibility (EMC) often demands the use of full-wave electromagnetic (EM) field-solvers to extract scattering parameters (S-parameters) for systems that contain discontinuities throughout their printed circuit boards (PCBs) and substrate packages. Using EM field-solvers not only requires extensive EM knowledge during the setup phase, but also demands considerable computational resources. Previously, machine learning (ML) and neural network (NN) models have been used to solve EM fields; however, they tend to use design parameters as input rather than the structure’s geometry.
Here, we present a functional convolutional NN which is trained on a single transition via in a typical multi-layered PCB substrate using only a three-dimensional (3D) voxelated grid representing the conductor and dielectric arrangement as input, and provides the 2-port S-Parameter matrix as output. Our NN has better than 1% Huber loss, while its computational time is 6,500 times faster than a conventional field-solver. Our NN-based modeling methodology serves as proof-of-concept for efficient approximates of S-parameters and may be considered as a fast and inexpensive tool compared to the expensive and often slower full-wave EM field-solvers.