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Recent Advances of Neural Network-Based EM-CAD

机译：基于神经网络的EM-CAD的最新进展

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摘要

In this article, we provide an overview of recent advances in computer-aided design techniques using neural networks for electromagnetic (EM) modeling and design applications. Summary of various recent neural network modeling techniques including passive component modeling, design and optimization using the models are discussed. Training data for the models are generated from EM simulations. The trained neural networks become fast and accurate models of EM structures. The models are then incorporated into various optimization methods and commercially available circuit simulators for fast design and optimization. We also provide an overview of recently developed neural network inverse modeling technique. Training a neural network inverse model directly may become difficult due to the nonuniqueness of the input-output relationship in the inverse model. Training data containing multivalued solutions are divided into groups according to derivative information. Multiple inverse submodels are built based on divided data groups and are then combined to form a complete model. Comparison between the conventional EM-based design approach and the inverse design approach has also been discussed. These computer-aided design techniques using neural models provide circuit level simulation speed with EM level accuracy avoiding the high computational cost of EM simulation.
机译：在本文中，我们概述了使用神经网络进行电磁（EM）建模和设计应用的计算机辅助设计技术的最新进展。讨论了各种最新的神经网络建模技术，包括被动组件建模，使用模型进行设计和优化。模型的训练数据是从EM仿真生成的。训练有素的神经网络成为EM结构的快速，准确模型。然后将模型合并到各种优化方法和可商购的电路仿真器中，以进行快速设计和优化。我们还提供了最近开发的神经网络逆建模技术的概述。由于逆模型中输入输出关系的不唯一性，直接训练神经网络逆模型可能会变得困难。包含多值解的训练数据根据派生信息分为几组。基于划分的数据组构建多个逆子模型，然后将其组合以形成一个完整的模型。还讨论了常规基于EM的设计方法和逆向设计方法之间的比较。这些使用神经模型的计算机辅助设计技术可提供具有EM级精度的电路级仿真速度，从而避免了EM仿真的高计算成本。

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