Artificial neural networks (ANNs) are information processing systems, with their design inspired by studies of the ability of the human brain to learn from observations and generalize by abstraction. Researchers have investigated a variety of important applications utilizing the ability of ANNs to perform the modeling and optimization of microwave components and circuits, such as high-speed very large-scale integration (VLSI) interconnects [1]-[3], spiral inductors [4], microwave field-effect transistors (FETs) [5], [6], heterojunction bipolar transistors [7], [8], high-electron mobility transistors [9], [10], filters [11]-[14], power amplifiers [15]-[17], oscillators [18], transmitters [19], receivers [20], digital predistortion [21], microelectromechanical systems [22], wireless power transfer [23], and multiphysics design [24], [25].

中文翻译：

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用于快速参数化 EM 建模的 ANN：用于无源微波结构设计自动化的机器学习的最新技术

人工神经网络 (ANN) 是信息处理系统，其设计灵感来自对人类大脑从观察中学习和通过抽象概括的能力的研究。研究人员已经研究了利用 ANN 的能力对微波元件和电路进行建模和优化的各种重要应用，例如高速超大规模集成 (VLSI) 互连 [1]-[3]、螺旋电感器 [ 4]、微波场效应晶体管 (FET) [5]、[6]、异质结双极晶体管 [7]、[8]、高电子迁移率晶体管 [9]、[10]、滤波器 [11]-[14] ]、功率放大器 [15]-[17]、振荡器 [18]、发射器 [19]、接收器 [20]、数字预失真 [21]、微机电系统 [22]、无线电力传输 [23] 和多物理场设计 [ 24]，[25]。