Citation: | Zhang Yang, Zhou Yang, Zhang Zehai, et al. PIN diode temperature characteristics prediction based on variational mode decomposition and autoencoder[J]. High Power Laser and Particle Beams, 2024, 36: 043013. doi: 10.11884/HPLPB202436.230237 |
[1] |
袁月乾, 陈自东, 马弘舸, 等. PIN限幅器的高功率微波单脉冲效应研究[J]. 强激光与粒子束, 2020, 32:063003 doi: 10.11884/HPLPB202032.190174
Yuan Yueqian, Chen Zidong, Ma Hongge, et al. High power microwave effect of PIN limiter induced by single pulse[J]. High Power Laser and Particle Beams, 2020, 32: 063003 doi: 10.11884/HPLPB202032.190174
|
[2] |
王明, 马弘舸. 组合脉冲内间隔对限幅器热损伤效应的影响[J]. 强激光与粒子束, 2018, 30:063002 doi: 10.11884/HPLPB201830.170426
Wang Ming, Ma Hongge. Influence of pulse interval on thermal damage process of PIN limiter[J]. High Power Laser and Particle Beams, 2018, 30: 063002 doi: 10.11884/HPLPB201830.170426
|
[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, 2005, 52(1): 1-4. doi: 10.1109/TCSII.2004.839537
|
[4] |
张永战, 孟凡宝, 赵刚. Ⅰ层厚度对限幅器热损伤效应的影响[J]. 强激光与粒子束, 2017, 29:093002 doi: 10.11884/HPLPB201729.170087
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, 2017, 29: 093002 doi: 10.11884/HPLPB201729.170087
|
[5] |
赵振国, 周海京, 马弘舸, 等. 微波脉冲频率与重频对限幅器热损伤效应的影响[J]. 强激光与粒子束, 2015, 27:103239 doi: 10.11884/HPLPB201527.103239
Zhao Zhenguo, Zhou Haijing, Ma Hongge, et al. Influence of frequency and microwave repetition rate on thermal damage process of PIN limiter[J]. High Power Laser and Particle Beams, 2015, 27: 103239 doi: 10.11884/HPLPB201527.103239
|
[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, 2019, 66(10): 4474-4477. doi: 10.1109/TED.2019.2937786
|
[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, 2021, 21(4): 455-464. doi: 10.1109/TDMR.2021.3116599
|
[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, 2021, 68(3): 1318-1325. doi: 10.1109/TED.2020.3048918
|
[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, 2021, 68(9): 4181-4188. doi: 10.1109/TED.2021.3093376
|
[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, 2021, 42(2): 136-139. doi: 10.1109/LED.2020.3045064
|