Vol. 149
Latest Volume
All Volumes
PIERC 149 [2024] PIERC 148 [2024] PIERC 147 [2024] PIERC 146 [2024] PIERC 145 [2024] PIERC 144 [2024] PIERC 143 [2024] PIERC 142 [2024] PIERC 141 [2024] PIERC 140 [2024] PIERC 139 [2024] PIERC 138 [2023] PIERC 137 [2023] PIERC 136 [2023] PIERC 135 [2023] PIERC 134 [2023] PIERC 133 [2023] PIERC 132 [2023] PIERC 131 [2023] PIERC 130 [2023] PIERC 129 [2023] PIERC 128 [2023] PIERC 127 [2022] PIERC 126 [2022] PIERC 125 [2022] PIERC 124 [2022] PIERC 123 [2022] PIERC 122 [2022] PIERC 121 [2022] PIERC 120 [2022] PIERC 119 [2022] PIERC 118 [2022] PIERC 117 [2021] PIERC 116 [2021] PIERC 115 [2021] PIERC 114 [2021] PIERC 113 [2021] PIERC 112 [2021] PIERC 111 [2021] PIERC 110 [2021] PIERC 109 [2021] PIERC 108 [2021] PIERC 107 [2021] PIERC 106 [2020] PIERC 105 [2020] PIERC 104 [2020] PIERC 103 [2020] PIERC 102 [2020] PIERC 101 [2020] PIERC 100 [2020] PIERC 99 [2020] PIERC 98 [2020] PIERC 97 [2019] PIERC 96 [2019] PIERC 95 [2019] PIERC 94 [2019] PIERC 93 [2019] PIERC 92 [2019] PIERC 91 [2019] PIERC 90 [2019] PIERC 89 [2019] PIERC 88 [2018] PIERC 87 [2018] PIERC 86 [2018] PIERC 85 [2018] PIERC 84 [2018] PIERC 83 [2018] PIERC 82 [2018] PIERC 81 [2018] PIERC 80 [2018] PIERC 79 [2017] PIERC 78 [2017] PIERC 77 [2017] PIERC 76 [2017] PIERC 75 [2017] PIERC 74 [2017] PIERC 73 [2017] PIERC 72 [2017] PIERC 71 [2017] PIERC 70 [2016] PIERC 69 [2016] PIERC 68 [2016] PIERC 67 [2016] PIERC 66 [2016] PIERC 65 [2016] PIERC 64 [2016] PIERC 63 [2016] PIERC 62 [2016] PIERC 61 [2016] PIERC 60 [2015] PIERC 59 [2015] PIERC 58 [2015] PIERC 57 [2015] PIERC 56 [2015] PIERC 55 [2014] PIERC 54 [2014] PIERC 53 [2014] PIERC 52 [2014] PIERC 51 [2014] PIERC 50 [2014] PIERC 49 [2014] PIERC 48 [2014] PIERC 47 [2014] PIERC 46 [2014] PIERC 45 [2013] PIERC 44 [2013] PIERC 43 [2013] PIERC 42 [2013] PIERC 41 [2013] PIERC 40 [2013] PIERC 39 [2013] PIERC 38 [2013] PIERC 37 [2013] PIERC 36 [2013] PIERC 35 [2013] PIERC 34 [2013] PIERC 33 [2012] PIERC 32 [2012] PIERC 31 [2012] PIERC 30 [2012] PIERC 29 [2012] PIERC 28 [2012] PIERC 27 [2012] PIERC 26 [2012] PIERC 25 [2012] PIERC 24 [2011] PIERC 23 [2011] PIERC 22 [2011] PIERC 21 [2011] PIERC 20 [2011] PIERC 19 [2011] PIERC 18 [2011] PIERC 17 [2010] PIERC 16 [2010] PIERC 15 [2010] PIERC 14 [2010] PIERC 13 [2010] PIERC 12 [2010] PIERC 11 [2009] PIERC 10 [2009] PIERC 9 [2009] PIERC 8 [2009] PIERC 7 [2009] PIERC 6 [2009] PIERC 5 [2008] PIERC 4 [2008] PIERC 3 [2008] PIERC 2 [2008] PIERC 1 [2008]
2024-10-19
A Novel Knowledge-Based Neural Network Approach to the Small-Signal Modeling of Packaged Diodes
By
Progress In Electromagnetics Research C, Vol. 149, 9-14, 2024
Abstract
This paper proposes a novel knowledge-based neural network approach that, in the absence of specific device SPICE models, can utilize the measured data of actual diode devices to map the existing diode coarse model to a more accurate package model through neural network mapping techniques, thereby achieving precise and efficient modeling of the small-signal characteristics of diode devices. A knowledge-based neural network model for packaged diodes is proposed, which enhances modeling accuracy by learning the discrepancies between the diode coarse model and the actual device data. A training method for rapid parameter adjustment is suggested, where the neural networks within the input and output packaging modules automatically learn and adjust, continuously optimizing their internal parameters to enhance modeling efficiency. Modeling experiments conducted on the measurement data of the MA4AGFCP910 diode show that the proposed packaged diode model can effectively and accurately match the small-signal characteristic data of the diode device.
Citation
Wenyuan Liu, Ningning Yang, Shuxia Yan, and Yanlin Xu, "A Novel Knowledge-Based Neural Network Approach to the Small-Signal Modeling of Packaged Diodes," Progress In Electromagnetics Research C, Vol. 149, 9-14, 2024.
doi:10.2528/PIERC24080901
References

1. Chen, Zhenzhen, Junquan Chen, and Xing Chen, "Analysis of a pin diode circuit at radio frequency using an electromagnetic-physics-based simulation method," Electronics, Vol. 12, No. 7, 1525, 2023.

2. Yin, Shan, Yunfei Gu, King Jet Tseng, Juntao Li, Gang Dai, and Kun Zhou, "A physics-based compact model of SiC junction barrier Schottky diode for circuit simulation," IEEE Transactions on Electron Devices, Vol. 65, No. 8, 3095-3103, 2018.

3. Zhao, Weiheng, Yudi Zhao, and Min Miao, "A physics-based modeling method of THz Schottky diode for circuit simulation," Microelectronics Journal, Vol. 136, 105775, 2023.

4. Li, Xufan, Zhenhua Wu, Gerhard Rzepa, Markus Karner, Haoqing Xu, Zhicheng Wu, Wei Wang, Guanhua Yang, Qing Luo, Lingfei Wang, and Ling Li, "Overview of emerging semiconductor device model methodologies: From device physics to machine learning engines," Fundamental Research, 2024.

5. Wang, Junfei, Junhui Hu, Chaowen Guan, Yuqi Hou, Leihao Sun, Songke Fang, Jianyang Shi, Ziwei Li, Junwen Zhang, Nan Chi, and Chao Shen, "Study of equivalent circuit of GaN based laser chip and packaged laser," Scientific Reports, Vol. 14, No. 1, 11368, 2024.

6. Zhang, Ao and Jianjun Gao, "Comprehensive analysis of linear and nonlinear equivalent circuit model for GaAs-PIN diode," IEEE Transactions on Industrial Electronics, Vol. 69, No. 11, 11541-11548, Nov. 2022.

7. Liu, Wenyuan, Lin Zhu, Weicong Na, and Qi-Jun Zhang, "An overview of neuro-space mapping techniques for microwave device modeling," 2016 IEEE MTT-S Latin America Microwave Conference (LAMC), 1-3, Puerto Vallarta, Mexico, Dec. 2016.

8. Feng, Feng, Weicong Na, Jing Jin, Jianan Zhang, Wei Zhang, and Qi-Jun Zhang, "Artificial neural networks for microwave computer-aided design: The state of the art," IEEE Transactions on Microwave Theory and Techniques, Vol. 70, No. 11, 4597-4619, 2022.

9. Cao, Yazi, Xi Chen, and Gaofeng Wang, "Dynamic behavioral modeling of nonlinear microwave devices using real-time recurrent neural network," IEEE Transactions on Electron Devices, Vol. 56, No. 5, 1020-1026, 2009.

10. Zhang, Qi-Jun and Kuldip C. Gupta, Neural Networks for RF and Microwave Design, Artech House, 2000.

11. Cao, Yazi, Xi Chen, and Gaofeng Wang, "Dynamic behavioral modeling of nonlinear microwave devices using real-time recurrent neural network," IEEE Transactions on Electron Devices, Vol. 56, No. 5, 1020-1026, 2009.

12. Fang, Yonghua, Mustapha C. E. Yagoub, Fang Wang, and Qi-Jun Zhang, "A new macromodeling approach for nonlinear microwave circuits based on recurrent neural networks," IEEE Transactions on Microwave Theory and Techniques, Vol. 48, No. 12, 2335-2344, 2000.

13. Yan, Shuxia, Xiaoyi Jin, Yaoqian Zhang, Weiguang Shi, and Jia Wen, "Neurospace mapping modeling for packaged transistors," Mathematical Problems in Engineering, Vol. 2018, No. 1, 4584069, 2018.

14. Yan, Shuxia, Xiaoyi Jin, Yaoqian Zhang, Weiguang Shi, and Jia Wen, "Accurate large-signal modeling using neuro-space mapping for power transistors," IEICE Electronics Express, Vol. 15, No. 14, 20180342, 2018.

15. Xu, Jianjun, M. C. E. Yagoub, Runtao Ding and Qi Jun Zhang, "Exact adjoint sensitivity analysis for neural-based microwave modeling and design," IEEE Transactions on Microwave Theory & Techniques, Vol. 51, No. 1, 226-237, 2001.