摘要
随着微波器件结构复杂度的增长和产品性能要求的提高,微波器件建模不仅要能够描述其理想电磁特性,还要能快速准确反映多物理参数对器件性能的影响。虽然神经网络已经被引入到微波器件领域,但是将其应用于器件的多物理特性建模的研究还比较少。文章提出了一种基于人工神经网络的多物理参数建模方法来表示输入输出变量之间的非线性关系。提出了一种高效的神经网络多物理参数模型,并针对该模型引入了一种新的训练算法。所提出的模型可以快速准确地预测微波器件的多物理响应,如滤波器的S参数特性曲线、离子敏感场效应晶体管的输出特性曲线等。与有限元方法相比,此方法可以节省约98%的计算成本与99%的计算时间,为实现快速高效的微波器件行为级建模提供一种可行方法。
With the increase of the complexity of microwave device structure and the requirement of product performance,microwave device modeling should not only be able to describe the ideal electromagnetic characteristics,but also be able to quickly and accurately reflect the influence of multiple physical parameters on device performance.Although neural networks have been introduced into the field of microwave devices,there are few studies on their application in the multi-physical modeling of devices.In this paper,a multi-physical parameter modeling method based on artificial neural network is proposed to represent the nonlinear relationship between input and output variables.An efficient neural network multi-physical parameter model is proposed,and a new training algorithm is introduced for the model.The proposed model can quickly and accurately predict the multi-physical response of microwave devices,such as the S-parameter characteristic curve of filters and the output characteristic curve of ion-sensitive field-effect transistor.Compared with the finite element method,this method can save about 98%of the calculation cost and 99%of the calculation time,which provides a feasible method for fast and efficient behavior-level modeling of microwave devices.
作者
谢佳楠
刘文远
王露洁
周远国
XIE Jia-nan;LIU Wen-yuan;WANG Lu-jie;ZHOU Yuan-guo(College of Communication and Information Engineering,Xi'an University of Science and Technology,Xi'an 710054,China;College of Electronic Information and Artificial Intelligence,Shaanxi University of Science and Technology,Xi'an 710021,China)
出处
《微波学报》
CSCD
北大核心
2022年第3期59-64,共6页
Journal of Microwaves
基金
陕西省重点研发计划(2018GY-151)
陕西省自然科学基础研究计划(2021JQ-573)
陕西省自然科学基金基础研究计划(2020JM-515)。
关键词
有限元方法
人工神经网络
多物理场
滤波器
离子敏感场效应晶体管
finite element method
artificial neural network
multi-physical field
filter
ion-sensitive field-effect transistor
