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 WuQinru Qiu, R. Stanley Williams, Qiangfei Xia, J. Joshua Yang

Research output: Contribution to journalArticlepeer-review

200 Scopus citations


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.

Original languageEnglish (US)
Article number3208
JournalNature Communications
Issue number1
StatePublished - Dec 1 2018

ASJC Scopus subject areas

  • General Chemistry
  • General Biochemistry, Genetics and Molecular Biology
  • General Physics and Astronomy


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