Researching | PIN diode temperature characteristics prediction based on variational mode decomposition and autoencoder

Journals >High Power Laser and Particle Beams >Volume 36 >Issue 4 >Page 043013 > Article

High Power Laser and Particle Beams
Vol. 36, Issue 4, 043013 (2024)

PIN diode temperature characteristics prediction based on variational mode decomposition and autoencoder

Yang Zhang, Yang Zhou, Zehai Zhang, Fuxiang Yang, and Xingjun Ge

Author Affiliations

College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China

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DOI: 10.11884/HPLPB202436.230237 Cite this Article

Yang Zhang, Yang Zhou, Zehai Zhang, Fuxiang Yang, Xingjun Ge. PIN diode temperature characteristics prediction based on variational mode decomposition and autoencoder[J]. High Power Laser and Particle Beams, 2024, 36(4): 043013 Copy Citation Text

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References

[1] Yuan Yueqian, Chen Zidong, Ma Hongge, . High power microwave effect of PIN limiter induced by single pulse[J]. High Power Laser and Particle Beams, 32, 063003(2020).

[2] Wang Ming, Ma Hongge. Influence of pulse interval on thermal damage process of PIN limiter[J]. High Power Laser and Particle Beams, 30, 063002(2018).

[3] Bera S C, Bharadhwaj P S. Insight into PIN diode behaviour leads to improved control circuit[J]. IEEE Transactions on Circuits and Systems II:Express Briefs, 52, 1-4(2005).

[4] Zhang Yongzhan, Meng Fanbao, Zhao Gang. Influence of I layer thickness on thermal damage process of PIN limiter[J]. High Power Laser and Particle Beams, 29, 093002(2017).

[5] Zhao Zhenguo, Zhou Haijing, Ma Hongge, . Influence of frequency and microwave repetition rate on thermal damage process of PIN limiter[J]. High Power Laser and Particle Beams, 27, 103239(2015).

[6] Ko K, Lee J K, Kang M, et al. Prediction of process variation effect for ultrascaled GAA vertical FET devices using a machine learning approach[J]. IEEE Transactions on Electron Devices, 66, 4474-4477(2019).

[7] Liang Wei, Yang Xuejiao, Miao Meng, et al. Novel ESD compact modeling methodology using machine learning techniques for snapback and non-snapback ESD devices[J]. IEEE Transactions on Device and Materials Reliability, 21, 455-464(2021).

[8] Wang Jing, Kim Y H, Ryu J, et al. Artificial neural network-based compact modeling methodology for advanced transistors[J]. IEEE Transactions on Electron Devices, 68, 1318-1325(2021).

[9] Yang Qihang, Qi Guodong, Gan Weizhuo, et al. Transistor compact model based on multigradient neural network and its application in SPICE circuit simulations for gate-all-around Si Cold source FETs[J]. IEEE Transactions on Electron Devices, 68, 4181-4188(2021).

[10] Mehta K, Wong H Y. Prediction of FinFET current-voltage and capacitance-voltage curves using machine learning with Autoencoder[J]. IEEE Electron Device Letters, 42, 136-139(2021).

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Yang Zhang, Yang Zhou, Zehai Zhang, Fuxiang Yang, Xingjun Ge. PIN diode temperature characteristics prediction based on variational mode decomposition and autoencoder[J]. High Power Laser and Particle Beams, 2024, 36(4): 043013

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