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- Title
Capacitive neural network with neuro-transistors.
- Authors
Wang, Zhongrui; Rao, Mingyi; Han, Jin-Woo; Zhang, Jiaming; Lin, Peng; Li, Yunning; Li, Can; Song, Wenhao; Asapu, Shiva; Midya, Rivu; Zhuo, Ye; Jiang, Hao; Yoon, Jung Ho; Upadhyay, Navnidhi Kumar; Joshi, Saumil; Hu, Miao; Strachan, John Paul; Barnell, Mark; Wu, Qing; Wu, Huaqiang
- 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. Though memristors can potentially emulate neuron and synapse functionality, useful signal energy is lost to Joule heating. Here, the authors demonstrate neuro-transistors with a pseudo-memcapacitive gate that actively process signals via energy-efficient capacitively-coupled neural networks.
- Publication
Nature Communications, 2018, Vol 9, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-018-05677-5