Qiangfei Xia

Contact

Prof. Qiangfei Xia

Department of Electrical and Computer Engineering

University of Massachusetts Amherst

Telephone:(413) 545-4571

Email: qxia@umass.edu

Research Field and Activities

Memristors & Memristive Devices
In-Memory Computing
Neuromorphic Engineering
Nanoelectronics & Nanoscale Devices

Short Biography

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.

References / Publications

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, and Q. Xia, “Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing,” Nature Materials, 2017. DOI: 10.1038/nmat4756.
Q. Xia and J.J. Yang, “Memristive crossbar arrays for brain-inspired computing,” Nature Materials, 2019. DOI: 10.1038/s41563-019-0291-x.
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, and Q. Xia, “Analogue signal and image processing with large memristor crossbars,” Nature Electronics, 2018. DOI: 10.1038/s41928-017-0002-z.
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, and Q. Xia, “Efficient and self-adaptive in-situ learning in multilayer memristor neural networks,” Nature Communications, 2018. DOI: 10.1038/s41467-018-04484-2.
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, and J.J. Yang, “Fully memristive neural networks for pattern classification with unsupervised learning,” Nature Electronics, 2018. DOI: 10.1038/s41928-018-0059-6.
Z. Wang, H. Wu, G.W. Burr, C.S. Hwang, K.L. Wang, Q. Xia, and J.J. Yang, “Resistive switching materials for information processing,” Nature Reviews Materials, 2020. DOI: 10.1038/s41578-019-0148-z.
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, and Q. Xia, “Black phosphorus mid-infrared photodetectors with high gain,” Nano Letters, 2016. DOI: 10.1021/acs.nanolett.6b01771.
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, and R.S. Williams, “Memristor-CMOS hybrid integrated circuits for reconfigurable logic,” Nano Letters, 2009. DOI: 10.1021/nl901874j.
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, and Q. Xia, “Memristor-based analog computation and neural network classification with a dot product engine,” Advanced Materials, 2018. DOI: 10.1002/adma.201705914.
S. Pi, S. Lin, and Q. Xia, “A fully hardware-based memristive multilayer neural network,” Science Advances, 2022. DOI: 10.1126/sciadv.abj4801.