Can Li, Ph.D.

2018-2020 Hewlett Packard Labs, Palo Alto, CA, United States 
 2020- Electrical and Electronic Engineering The University of Hong Kong, Hong Kong Island, Hong Kong, Hong Kong 
Neuromorphic computing
"Can Li"
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Li Y, Song W, Wang Z, et al. (2022) Memristive Field-Programmable Analog Arrays for Analog Computing. Advanced Materials (Deerfield Beach, Fla.). e2206648
Xia Q, Berggren KK, Likharev K, et al. (2020) Roadmap on emerging hardware and technology for machine learning. Nanotechnology
Pi S, Li C, Jiang H, et al. (2018) Memristor crossbar arrays with 6-nm half-pitch and 2-nm critical dimension. Nature Nanotechnology
Wang Z, Rao M, Han JW, et al. (2018) Capacitive neural network with neuro-transistors. Nature Communications. 9: 3208
Mehonic A, Shluger AL, Gao D, et al. (2018) Silicon Oxide (SiO ): A Promising Material for Resistance Switching? Advanced Materials (Deerfield Beach, Fla.). e1801187
Li C, Belkin D, Li Y, et al. (2018) Efficient and self-adaptive in-situ learning in multilayer memristor neural networks. Nature Communications. 9: 2385
Hu M, Graves CE, Li C, et al. (2018) Memristor-Based Analog Computation and Neural Network Classification with a Dot Product Engine. Advanced Materials (Deerfield Beach, Fla.)
Jiang H, Belkin D, Savel'ev SE, et al. (2017) A novel true random number generator based on a stochastic diffusive memristor. Nature Communications. 8: 882
Li C, Han L, Jiang H, et al. (2017) Three-dimensional crossbar arrays of self-rectifying Si/SiO2/Si memristors. Nature Communications. 8: 15666
Midya R, Wang Z, Zhang J, et al. (2017) Anatomy of Ag/Hafnia-Based Selectors with 10(10) Nonlinearity. Advanced Materials (Deerfield Beach, Fla.)
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