Shuxia Yan, Xu Dong, Xiaoyi Jin, Weiguang Shi, W. Xu
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引用次数: 1
摘要
综述了基于神经空间映射(neural - space Mapping,简称neurosm)的微波器件非线性建模技术。本文主要介绍了两种神经网络模型:带输入映射网络的神经网络模型和带输出映射网络的神经网络模型。与传统等效电路模型相比,neural - sm模型具有更高的精度。以射频功率横向扩散金属氧化物半导体(LDMOS)晶体管和InP HEMT晶体管的测量数据为应用实例,验证了所综述的两种neural - sm模型能够准确地反映晶体管的特性,操作过程简单,提高了现有模型的精度。
Review of Neuro-Space Mapping Method for Transistor Modeling
This paper reviews the nonlinear microwave device modeling technology based on Neuro-Space Mapping (Neuro-SM). We mainly introduce two kinds of Neuro-SM models: the Neuro-SM model with input mapping network and the Neuro-SM model with output mapping network. Compared with the traditional equivalent circuit model, the Neuro-SM models are more accurate. Measurement data of the RF power laterally diffuse metal-oxide semiconductor (LDMOS) transistor and InP HEMT transistor are used as the application examples to verify the reviewed two Neuro-SM models can accurately reflect the characteristics of transistors with simple operation process and enhance the accuracy of the existing model.