Capacitive neural network with neuro-transistors

Zhongrui Wang, Mingyi Rao, Jin-Woo Han, Jiaming Zhang, Peng Lin, Yunning Li, Can Li, Wenhao Song, Shiva Asapu, Rivu Midya, Ye Zhuo, Hao Jiang, Jung Ho Yoon, Navnidhi Kumar Upadhyay, Saumil Joshi, Miao Hu, John Paul Strachan, Mark Barnell, Qing Wu, Huaqiang Wu, Qinru Qiu, R. Stanley Williams (), Qiangfei Xia () and J. Joshua Yang ()
Additional contact information
Zhongrui Wang: University of Massachusetts
Mingyi Rao: University of Massachusetts
Jin-Woo Han: NASA Ames Research Center
Jiaming Zhang: Hewlett-Packard Laboratories
Peng Lin: University of Massachusetts
Yunning Li: University of Massachusetts
Can Li: University of Massachusetts
Wenhao Song: University of Massachusetts
Shiva Asapu: University of Massachusetts
Rivu Midya: University of Massachusetts
Ye Zhuo: University of Massachusetts
Hao Jiang: University of Massachusetts
Jung Ho Yoon: University of Massachusetts
Navnidhi Kumar Upadhyay: University of Massachusetts
Saumil Joshi: University of Massachusetts
Miao Hu: Hewlett-Packard Laboratories
John Paul Strachan: Hewlett-Packard Laboratories
Mark Barnell: Information Directorate
Qing Wu: Information Directorate
Huaqiang Wu: Tsinghua University
Qinru Qiu: Syracuse University
R. Stanley Williams: Hewlett-Packard Laboratories
Qiangfei Xia: University of Massachusetts
J. Joshua Yang: University of Massachusetts

Nature Communications, 2018, vol. 9, issue 1, 1-10

Abstract: Abstract Experimental demonstration of resistive neural networks has been the recent focus of hardware implementation of neuromorphic computing. Capacitive neural networks, which call for novel building blocks, provide an alternative physical embodiment of neural networks featuring a lower static power and a better emulation of neural functionalities. Here, we develop neuro-transistors by integrating dynamic pseudo-memcapacitors as the gates of transistors to produce electronic analogs of the soma and axon of a neuron, with “leaky integrate-and-fire” dynamics augmented by a signal gain on the output. Paired with non-volatile pseudo-memcapacitive synapses, a Hebbian-like learning mechanism is implemented in a capacitive switching network, leading to the observed associative learning. A prototypical fully integrated capacitive neural network is built and used to classify inputs of signals.

Date: 2018
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DOI: 10.1038/s41467-018-05677-5

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