Chua Memristor CenterProf. Qiangfei Xia
Department of Electrical and Computer Engineering
University of Massachusetts Amherst
Email: qxia@umass.edu
Professor Qiangfei Xia is the Dev and Linda Gupta Professor of Electrical and Computer Engineering at the University of Massachusetts Amherst. He is internationally recognized for his pioneering work in memristive devices, neuromorphic computing, and in-memory processing. His research focuses on building nanoscale electronic systems that mimic the brain’s computing efficiency and enable next-generation artificial intelligence hardware.
He earned his Ph.D. in Electrical Engineering from Princeton University and previously worked at HP Labs in Palo Alto, where he contributed significantly to the development of memristor technology. At UMass Amherst, he leads the Nanoelectronics Group, exploring cutting-edge innovations in nanoelectronics, resistive memory (RRAM), and AI accelerators.
“Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing”
Authors: Z. Wang, S. Joshi, S.E. Savel’ev, H. Jiang, R. Midya, P. Lin, M. Hu, N. Ge, J.P. Strachan, Z. Li, Q. Wu, M. Barnell, G.-L. Li, H. Xin, R.S. Williams, J.J. Yang, Q. Xia
https://doi.org/10.1038/nmat4756
“Memristive crossbar arrays for brain-inspired computing”
Authors: Q. Xia, J.J. Yang
https://doi.org/10.1038/s41563-019-0291-x
“Analogue signal and image processing with large memristor crossbars”
Authors: C. Li, M. Hu, Y. Li, H. Jiang, N. Ge, E. Montgomery, J. Zhang, W. Song, P. Lin, Z. Wang, N. Davila, C.E. Graves, Z. Li, J.P. Strachan, M. Barnell, Q. Wu, R.S. Williams, J.J. Yang, Q. Xia
https://doi.org/10.1038/s41928-017-0002-z
“Efficient and self-adaptive in-situ learning in multilayer memristor neural networks”
Authors: C. Li, D. Belkin, Y. Li, P. Yan, M. Hu, N. Ge, H. Jiang, E. Montgomery, P. Lin, Z. Wang, W. Song, N. Davila, C.E. Graves, Z. Li, J.P. Strachan, M. Barnell, Q. Wu, R.S. Williams, J.J. Yang, Q. Xia
https://doi.org/10.1038/s41467-018-04484-2
“Fully memristive neural networks for pattern classification with unsupervised learning”
Authors: Z. Wang, S. Joshi, S. Savel’ev, W. Song, R. Midya, Y. Li, M. Rao, P. Yan, J.P. Strachan, M. Barnell, Q. Wu, M. Hu, H. Jiang, N. Ge, R.S. Williams, Q. Xia, J.J. Yang
https://doi.org/10.1038/s41928-018-0059-6
“Resistive switching materials for information processing”
Authors: Z. Wang, H. Wu, G.W. Burr, C.S. Hwang, K.L. Wang, Q. Xia, J.J. Yang
https://doi.org/10.1038/s41578-019-0148-z
“Black phosphorus mid-infrared photodetectors with high gain”
Authors: Q. Guo, A. Pospischil, M. Bhuiyan, H. Jiang, H. Tian, D. Farmer, B. Deng, C. Li, S. Han, H. Wang, X. Xia, T. Mueller, F. Xia, Q. Xia
https://doi.org/10.1021/acs.nanolett.6b01771
“Memristor-CMOS hybrid integrated circuits for reconfigurable logic”
Authors: Q. Xia, W. Robinett, M.W. Cumbie, N. Banerjee, T.J. Cardinali, J.J. Yang, W. Wu, X. Li, W.M. Tong, D.B. Strukov, G.S. Snider, R.S. Williams
https://doi.org/10.1021/nl901874j
“Memristor-based analog computation and neural network classification with a dot product engine”
Authors: M. Hu, C.E. Graves, C. Li, Y. Li, N. Ge, E. Montgomery, N. Davila, H. Jiang, P. Lin, C. Wu, J. Zhang, Z. Wang, C.E. Graves, Z. Li, J.P. Strachan, M. Barnell, Q. Wu, R.S. Williams, J.J. Yang, Q. Xia
https://doi.org/10.1002/adma.201705914
“A fully hardware-based memristive multilayer neural network”
Authors: S. Pi, S. Lin, Q. Xia
https://doi.org/10.1126/sciadv.abj4801
