A low-power vertical dual-gate neurotransistor with short-term memory for high energy-efficient neuromorphic computing.

Xu, Han; Shang, Dashan; Luo, Qing; An, Junjie; Li, Yue; Wu, Shuyu; Yao, Zhihong; Zhang, Woyu; Xu, Xiaoxin; Dou, Chunmeng; Jiang, Hao; Pan, Liyang; Zhang, Xumeng; Wang, Ming; Wang, Zhongrui; Tang, Jianshi; Liu, Qi; Liu, Ming

Neuromorphic computing aims to emulate the computing processes of the brain by replicating the functions of biological neural networks using electronic counterparts. One promising approach is dendritic computing, which takes inspiration from the multi-dendritic branch structure of neurons to enhance the processing capability of artificial neural networks. While there has been a recent surge of interest in implementing dendritic computing using emerging devices, achieving artificial dendrites with throughputs and energy efficiency comparable to those of the human brain has proven challenging. In this study, we report on the development of a compact and low-power neurotransistor based on a vertical dual-gate electrolyte-gated transistor (EGT) with short-term memory characteristics, a 30 nm channel length, a record-low read power of ~3.16 fW and a biology-comparable read energy of ~30 fJ. Leveraging this neurotransistor, we demonstrate dendrite integration as well as digital and analog dendritic computing for coincidence detection. We also showcase the potential of neurotransistors in realizing advanced brain-like functions by developing a hardware neural network and demonstrating bio-inspired sound localization. Our results suggest that the neurotransistor-based approach may pave the way for next-generation neuromorphic computing with energy efficiency on par with those of the brain. Dendritic computing is a promising approach to enhance the processing capability of artificial neural networks. Here, the authors report the development of a neurotransistor based on a vertical dual-gate electrolyte-gated transistor with short-term memory characteristics, a 30 nm channel length, a low read power of ~3.16 fW and read energy of ~30 fJ for dendritic computing.

SHORT-term memory; PROCESS capability; ARTIFICIAL neural networks; BIOLOGICAL neural networks; DIRECTIONAL hearing

Nature Communications, 2023, Vol 14, Issue 1, p1

2041-1723

Academic Journal

10.1038/s41467-023-42172-y