个人简历

工作经历：\r
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2018年12月 － 今 教授，物理科学与技术学院，武汉大学\r
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2011年12月 － 2018年11月 副教授，物理科学与技术学院，武汉大学\r
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2007年12月 － 2011年11月 讲师，物理科学与技术学院，武汉大学\r
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2004年12月 － 2007年11月 助教，物理科学与技术学院，武汉大学\r
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2005年 9月 － 2009年 5月 博士，微电子学与固体电子学，武汉大学

研究领域

以微电子器件与电路为核心结合机器学习展开理论和应用研究。\r
一、神经形态人工智能的机制、电路及系统的研究与应用 类脑计算及新型机器学习机理 人工智能的电路级设计及硬件加速 人工智能的应用 \r
二、 低维纳米器件设计及其在电路中的应用 低维纳米体系特性计算 新型微纳器件结构设计及建模 基于新器件的电路设计及应用""

近期论文

S. Ye, Y. Lv, Z. Tang, R. Hu, R. Zhu, Z. Wang, Q. Huang, H. Wang, J. He and S. Chang*, “Wave-function symmetry mechanism of quantum-well states in graphene nanoribbon heterojunctions,” Physical Review Applied, Vol.12, 044018, 2019. (SCI一区)\r
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S. Wang, P. Lin, R. Hu, H. Wang, J. He, Q. Huang, S. Chang*, “Acceleration of LSTM with Structured Pruning Method on FPGA,” IEEE Access, Vol.7, No.1, pp. 62930-62937, 2019. (SCI二区)\r
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R. Zhu, S. Ye, Z. Tang, P. Lin, Q. Huang, H. Wang, J. He and S. Chang*, “Influence on Compact Memristors’ Stability on Machine Learning,” IEEE Access, Vol.7, No.1, pp. 47472-47478, 2019. (SCI二区)\r
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Y. Lv, Q. Huang, S. Chang*, H. Wang, J. He, C. Wei, A. Liu, S. Ye and W. Wang, “Interface Coupling as a Crucial Factor for Spatial Localization of Electronic States in a Heterojunction of Graphene Nanoribbons,” Physical Review Applied, Vol.11, 024026, 2019. (SCI一区)\r
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R. Hu, Q. Huang, H. Wang, J. He, and S. Chang*, “Monitor-Based Spiking Recurrent Network for the Representation of Complex Dynamic Patterns,” International Journal of Neural Systems, Vol.29, No.9, 1950006, 2019. (SCI一区)\r
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Z. Tang, R. Zhu, P. Lin, J. He, H. Wang, Q. Huang, S. Chang* and Q. Ma*, “A hardware friendly unsupervised memristive neural network with weight sharing mechanism,” Neurocomputing, Vol.332, pp.193-202, 2019. (SCI二区)\r
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Y. Lv, S. Ye, H. Wang, J. He, Q. Huang and S. Chang*, “Strain engineering of chevron graphene nanoribbons,” Journal of Applied Physics, Vol.125, 082501, 2019. (DOI:10.1063/1.5048527, online first). (SCI三区)\r
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R. Hu, S. Chang*, H. Wang, Q. Huang and J. He, “Efficient Multispike Learning for Spiking Neural Networks Using Probability-Modulated Timing Method,” IEEE Transactions on Neural Networks and Learning Systems, Vol.30, No.7, pp1984-1997, 2019. (SCI一区)\r
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S. Ye, R. Zhu, Q. Huang, J. He, H. Wang, Y. Lv and S. Chang*, “A Transport Isolation by Orbital Hybridization Transformation toward Graphene Nanoribbon-based Nanostructure Integration,” Nanotechnology, Vol.29, 455704, 2018. (SCI二区)\r
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Y. Lv, A. Liu, Q. Huang, S. Chang*, W. Qin, S. Ye, H. Wang and J. He, “Restraining Strategy of the Stone–Wales Defect Effect on Graphene Nanoribbon MOSFETs,” IEEE Electron Device Letters, Vol.39, No.7, pp.1092-1095, 2018. (SCI二区)\r
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Y. Lv, Q. Huang, S. Chang*, H. Wang, J. He, A. Liu, S. Ye and W. Wang, “Activating Impurity Effect in Edge Nitrogen-doped Chevron Graphene Nanoribbons,” Journal of Physics Communications, Vol.2, No.4, 045028, 2018.(新刊，ESCI)\r
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H. Wang, S. Chang*, Q. Huang and J. He, “Dielectric Engineering with the Environment Material in 2D Semiconductor Devices,”IEEE Journal of the Electron Device Society, Vol. 6, No.1, pp. 325-331, 2018. (SCI三区)\r
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P. Lin, S. Chang*, H. Wang, Q. Huang and J. He, “SpikeCD: A Parameter-insensitive Spiking Neural Network with Clustering Degeneracy Strategy,” Neural Computing Applications, (DOI: 10.1007/s00521-017-3336-6, early access). (SCI二区)\r
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R. Zhu, S. Chang*, H. Wang, Q. Huang, J. He and F. Yi, “A Versatile and Accurate Compact Model of Memristor with Equivalent Resistor Topology,” IEEE Electron Device Letters, Vol.38, No.10, pp.1367-1370, 2017. (SCI二区)\r
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Y. Lv, Q. Huang, S. Chang*, H. Wang and J. He, “Highly Sensitive Bilayer Phosphorene Nanoribbon Pressure Sensor Based on the Energy Gap Modulation Mechanism: A Theoretical Study,” IEEE Electron Device Letters, Vol.38, No.9, pp.1313-1316, 2017. (SCI二区)\r
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Y. Ji, S. Chang*, H. Wang, Q. Huang, J. He and F. Yi, \