A Reliable All‐2D Materials Artificial Synapse for High Energy‐Efficient Neuromorphic Computing. (24th March 2021) - Tang, Jian; He, Congli; Tang, Jianshi; Yue, Kun; Zhang, Qingtian; Liu, Yizhou; Wang, Qinqin; Wang, Shuopei; Li, Na; Shen, Cheng; Zhao, Yanchong; Liu, Jieying; et al.
Abstract: High‐performance artificial synaptic devices are indispensable for developing neuromorphic computing systems with high energy efficiency. However, the reliability and variability issues of existing devices such as nonlinear and asymmetric weight update are the major hurdles in their practical applications for energy‐efficient neuromorphic computing. Here, a two‐terminal floating‐gate memory (2TFGM) based artificial synapse built from all‐2D van der Waals materials is reported. The 2TFGM synaptic device exhibits excellent linear and symmetric weight update characteristics with high reliability and tunability. In particular, the high linearity and symmetric synaptic weight realized by simple programming with identical pulses can eliminate the additional latency and power consumption caused by the peripheral circuit design and achieve an ultralow energy consumption for the synapses in the neural network implementation. A large number of states up to ≈3000, high switching speed of 40 ns and low energy consumption of 18 fJ for a single pulse have been demonstrated experimentally. A high classification accuracy up to 97.7% (close to the software baseline of 98%) has been achieved in the Modified National Institute of Standards and Technology (MNIST) simulations based on the experimental data. These results demonstrate the potential of all‐2D 2TFGM for high‐speed and low‐power neuromorphic computing. Abstract : An all‐2D materials artificial synapse device is reported, which exhibits linear and symmetric weight update characteristics with high reliability and tunability. A large number of states up to ≈3000, high switching speed of 40 ns, and low energy consumption of 18 fJ for a single pulse are demonstrated. These results demonstrate the potential for high‐speed and low‐power neuromorphic computing applications.