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Qiangfei Xia

  • Energy-efficient hardware systems for machine intelligence, security, sensing and communication
  • Emerging nanoelectronic devices: design, characterization and understanding
  • Enabling fabrication and three-dimensional heterogeneous integration technologies

Current Research
Our lab is developing compact, fast and power efficient personal health monitors. The technology can potentially solve problems in data storage, diagnosis time and power consumption for these sensors. Another example is a one-drop device that could provide on-the-spot cancer detection, glucose testing, or iron counts, etc.

As data is continuously collected from personal health monitors, high density, large capacity, and fast data storage with low power consumption become a critical component. We are working on a novel non-volatile memory technology called the “resistance switch” or “memristor”, which allows for universal data storage that is more densely packed and information be stored to the device without a constant power source.

We are also developing nanofluidic channels that enable the transport and analysis of tiny biological molecules. These nanoscale channels will be integrated with terahertz circuits for on-the-spot diagnosis of cancer and other diseases, leading to faster and improved treatments.

With the two technologies combined, patients may soon be able to take potentially life-saving measurements continuously throughout the day.

Learn more at nano.ecs.umass.edu

Academic Background

  • BE Shanghai Jiao Tong University, 1998
  • MS Shanghai Jiao Tong University, 2001
  • PhD Princeton University, 2007
“Three-dimensional memristor circuits as complex neural networks”, P. Lin, C. Li, Z. Wang, Y. Li, H. Jiang, W. Song, M. Rao, Y. Zhuo, N.K. Upadhyay, M. Barnell, Q. Wu, J.J. Yang and Qiangfei Xia Nature Electronics 3, 225-232(2020). (Cover article). DOI:10.1038/s41928-020-0397-9
“Resistive switching materials for information processing”, Z. Wang, H. Wu, G.W. Burr, C.S. Hwang, K.L. Wang, Qiangfei Xia and J.J. Yang. Nature Reviews Materials 5, 173-195(2020). (Invited Review) DOI:10.1038/s41578-019-0159-3
“Memristive crossbar arrays for brain-inspired computing”, Qiangfei Xia and J.J. Yang Nature Materials 18, 309(2019). (Invited Review) DOI: 10.1038/s41563-019-0291-x
“In situ training of feed-forward and recurrent convolutional memristor networks”, Z. Wang, C. Li, P. Lin, M. Rao, Y. Nie, W. Song, Q. Qiu, Y. Li, P. Yan, J.P. Strachan, N. Ge, N. McDonald, Q. Wu, M. Hu, H. Wu, R.S. Williams, Qiangfei Xia and J.J. Yang
“Memristor crossbar arrays with 6-nm half-pitch and 2-nm critical dimension”, S. Pi, C. Li, H. Jiang, W. Xia, H.L. Xin, J.J. Yang and Qiangfei Xia Nature Nanotechnology 14, 35-39(2019). DOI: 10.1038/s41565-018-0302-0
“Reinforcement learning with analogue memristor arrays”, Z. Wang, C. Li, W. Song, M. Rao, D. Belkin, Y. Li, P. Yan, H. Jiang, P. Lin, M. Hu, J.P. Strachan, N. Ge, M. Barnell, Q. Wu, A.G. Barto, Q. Qiu, R.S. William, Qiangfei Xia, and J.J. Yang Nature Electronics 2, 115-124(2019). DOI:10.1038/s41928-019-0221-6
“Long short-term memory networks in memristor crossbar arrays”, C. Li, Z. Wang, M. Rao, D. Belkin, W. Song, H. Jiang, P. Yan, Y. Li, P. Lin, M. Hu, N. Ge, J.P. Strachan, M. Barnell, Q. Wu, R.S. Williams, J.J. Yang and Qiangfei Xia Nature Machine Intelligence 1, 49-57(2019). DOI: 10.1038/s42256-018-0001-4
“A provable key destruction scheme based on memristor crossbar arrays”, H. Jiang, C. Li, R. Zhang, P. Yan, P. Lin, Y. Li, J. J. Yang, D. Holcomb, and Qiangfei Xia. Nature Electronics, 1, 548-554 (2018). DOI: 10.1038/s41928-018-0146-5
“Efficient and self-adaptive in-situ learning in multilayer memristor neural networks”, C. Li, D. Belkin, Y. Li, P. Yan, M. Hu, N. Ge, H. Jiang, E. Montgomery, P. Lin, Z. Wang, J. P. Strachan, M. Barnell, Q. Wu, R.S. Williams, J.J. Yang & Qiangfei Xia. Nature Communications 9, 2385 (2018). DOI: 10.1038/s41467-018-04484-2
“Fully memristive neural networks for pattern classification with unsupervised learning”, Z. Wang, S. Joshi, S. Saveliev, W. Song, R. Midya, M. Rao, Y. Li, P. Yan, S. Asapu, Y. Zhuo, H. Jiang, P. Lin, C. Li, J.H. Yoon, N.K. Upadhyay, J. Zhang, M. Hu, J.P. Strachan, M. Barnell, Q. Wu, H. Wu, R. S. Williams, Qiangfei Xia, J.J. Yang. Nature Electronics 1, 137-145 (2018). DOI: 10.1038/s41928-018-0023-2
“Analogue signal and image processing with large memristor crossbars”, C. Li, M. Hu, Y. Li, H. Jiang, N. Ge, E. Montgomery, J. Zhang, W. Song, N. Dávila, C.E. Graves, Z. Li, J. P. Strachan, P. Lin, Z. Wang, M. Barnell, Q. Wu, R.S. Williams, J.J. Yang, and Qiangfei Xia Nature Electronics 1, 52-59 (2018). DOI: 10.1038/s41928-017-0002-z
“A novel true random number generator based on a stochastic diffusive memristor”, H. Jiang, D. Belkin, S.E. Savel'ev, S. Lin, Z. Wang, Y. Li, S. Joshi, R. Midya, C. Li, M. Rao, M. Barnell, Q. Wu, J.J. Yang, and Qiangfei Xia Nature Communications 8, 882 (2017). DOI: 10.1038/s41467-017-00869-x
“Three-dimensional crossbar arrays of self-rectifying Si/SiO2/Si memristors”, C. Li, L. Han, H. Jiang, M. Jang, P. Lin, Q. Wu, M. Barnell, J. J. Yang, H. L. Xin, and Qiangfei Xia Nature Communications 8, 15666 (2017). DOI: 10.1038/ncomms15666
“Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing”, 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. L. Xin, R. S. Williams, Qiangfei Xia, and J.J. Yang Nature Materials 16, 101-108 (2017). DOI: 10.1038/nmat4756
“Sub-10 nm Ta channel responsible for superior performance of a HfO2 memristor”, H. Jiang, L. Han, P. Lin, Z. Wang, M. Jang, Q. Wu, M. Barnell, J.J. Yang, H.L Xin, and Qiangfei Xia Scientific Reports 6, 28525 (2016). DOI: 10.1038/srep28525
“Nanoscale memristive radio-frequency switches”, S. Pi, M. Ghadiri-Sadrabadi, J.C. Bardin, and Qiangfei Xia Nature Communications 6, 7519 (2015). DOI: 10.1038/ncomms8519

Emerging nanoelectronic devices, Nanofluidic channels, Hybrid nano/CMOS systems for biomedical applications, Enabling nanofabrication and integration technologies. Learn more at nano.ecs.umass.edu

Contact Info

Electrical and Computer Engineering
201D Marcus Hall
100 Natural Resources Road
Amherst, MA 01003

(413) 545-4571