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您的检索式：作者名="Xingmin Hou"
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| 1 | Influence of electrode spacing and gas pressure on parameters of a runaway electron beam generating during the nanosecond breakdown in SF_(6) and nitrogen显示文摘This study deals with experimental and theoretical simulation data showing the influence of electrode spacing and gas pressure on parameters of a supershort avalanche electron beam(SAEB)formed in SF_(6) and nitrogen at different rise times and amplitudes of a voltage pulse.Using GIN-55-01,VPG-30-200,and SLEP-150M pulsers,tubular cathodes with a diameter of 6 mm,as well as gaps of 3,5,and 8 mm,it was shown that the SAEB current amplitude can both increase and decrease depending on an electrode spacing,a waveform and a rise time of the voltage pulse,as well as the pressure of SF6 and nitrogen.It was established as a result of simulation that maximal voltage across the gap during the process of generation of runaway electrons and the thickness of an anode foil have a major effect on the SAEB current pulse amplitude. | Victor F.Tarasenko Cheng Zhang Andrey V.Kozyrev Dmitry A.Sorokin Xingmin Hou Natalya S.Semeniuk Alexander G.Burachenko Ping Yan Vasily Yu.Kozhevnikov Evgenii Kh.Baksht Mikhail I.Lomaev Tao Shao | 2017 | High Voltage2017，2，2： | 1 |
| 2 | Experimental search for high-performance ferroelectric tunnel junctions guided by machine learning显示文摘Ferroelectric tunnel junction(FTJ)has attracted considerable attention for its potential applications in nonvolatile memory and neuromorphic computing.However,the experimental exploration of FTJs with high ON/OFF ratios is a challenging task due to the vast search space comprising of ferroelectric and electrode materials,fabrication methods and conditions and so on.Here,machine learning(ML)is demonstrated to be an effective tool to guide the experimental search of FTJs with high ON/OFF ratios.A dataset consisting of 152 FTJ samples with nine features and one target attribute(i.e.,ON/OFF ratio)is established for ML modeling.Among various ML models,the gradient boosting classification model achieves the highest prediction accuracy.Combining the feature importance analysis based on this model with the association rule mining,it is extracted that the utilizations of{graphene/graphite(Gra)(top),LaNiO_(3)(LNO)(bottom)}and{Gra(top),Ca_(0.96)Ce_(0.04)MnO_(3)(CCMO)(bottom)}electrode pairs are likely to result in high ON/OFF ratios in FTJs.Moreover,two previously unexplored FTJs:Gra/BaTiO_(3)(BTO)/LNO and Gra/BTO/CCMO,are predicted to achieve ON/OFF ratios higher than 1000.Guided by the ML predictions,the Gra/BTO/LNO and Gra/BTO/CCMO FTJs are experimentally fabricated,which unsurprisingly exhibit≥1000 ON/OFF ratios(~8540 and~7890,respectively).This study demonstrates a new paradigm of developing high-performance FTJs by using ML. | Jingjing Rao Zhen Fan Qicheng Huang Yongjian Luo Xingmin Zhang Haizhong Guo Xiaobing Yan Guo Tian Deyang Chen Zhipeng Hou Minghui Qin Min Zeng Xubing Lu Guofu Zhou Xingsen Gao Jun-Ming Liu | 2022 | Journal of Advanced Dielectrics2022，12，3： | 0 |
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