A Novel Small-Signal Knowledge-Based Neural Network Modeling Approach for Packaged Transistors
Journal Title: IOSR Journals (IOSR Journal of Electrical and Electronics Engineering) - Year 2018, Vol 13, Issue 5
Abstract
This paper proposes a novel small-signal knowledge-based neural network modeling method for packaged transistors. Separate neural networks are proposed to represent the behaviors of packages covering the core transistor. An advanced training method is developed by utilizing the different parameters to adjust the different characteristics of the packaged transistors, which avoid parameter adjustment repeatedly and speed up the modeling process. The proposed model combing the neural networks with the core transistor model is trained to present the entire small-signal behavior of the packaged transistors. Measurement data of the radio frequency (RF) power laterally diffused metal-oxide semiconductor (LDMOS) transistor are used as the application example to verify the capability of the proposed method. The results demonstrate that the proposed model is more accurate than existing models.
Authors and Affiliations
Shuxia Yan, Xiaoyi Jin, Yaoqian Zhang, Weiguang Shi, Jia Wen
Keywords
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- EP ID EP395781
- DOI 10.9790/1676-1305014045.
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