Capacitive neural network with neuro-transistors
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 10, 2018
- Source ID
- 10.1038/s41467-018-05677-5
Entities
People
- Can Li
- Hao Jiang
- Huaqiang Wu
- J. Joshua Yang
- Jiaming Zhang
- Jin-Woo Han
- John Paul Strachan
- Jung Ho Yoon
- Mark Barnell
- Miao Hu
- Mingyi Rao
- Navnidhi Kumar Upadhyay
- Peng Lin
- Qiangfei Xia
- Qing Wu
- Qinru Qiu
- R. Stanley Williams
- Rivu Midya
- Saumil Joshi
- Shiva Asapu
- Wenhao Song
- Ye Zhuo
- Yunning Li
- Zhongrui Wang
Organizations
- Air Force Research Laboratory