A Flexible Mott Synaptic Transistor for Nociceptor Simulation and Neuromorphic Computing. (30th March 2021) - Deng, Xing; Wang, Si‐Qi; Liu, Yu‐Xiang; Zhong, Ni; He, Yu‐Hui; Peng, Hui; Xiang, Ping‐Hua; Duan, Chun‐Gang
Abstract: Designing transparent flexible electronics with multi‐biological neuronal functions and superior flexibility is a key step to establish wearable artificial intelligence equipment. Here, a flexible ionic gel‐gated VO2 Mott transistor is developed to simulate the functions of the biological synapse. Short‐term and long‐term plasticity of the synapse are realized by the volatile electrostatic carrier accumulation and nonvolatile proton‐doping modulation, respectively. With the achievement of multi‐essential synaptic functions, an important sensory neuron, nociceptor, is perfectly simulated in our synaptic transistors with all key characteristics of threshold, relaxation, and sensitization. More importantly, this synaptic transistor exhibits high tolerance to the bending deformation, and the cycle‐to‐cycle variations of multi‐conductance states in potentiation and depression properties are maintained within 4%. This superior stability further indicates that our flexible device is suitable for neuromorphic computing. Simulation results demonstrate that high recognition accuracy of handwritten digits (>95%) can be achieved in a convolution neural network built from these synaptic transistors. The transparent and flexible Mott transistor based on electrically‐controlled VO2 metal‐insulator transition is believed to open up alternative approaches to developing highly stable synapses for future flexible neuromorphic systems. Abstract : A flexible Mott synaptic transistor is developed to simulate the functions of a biological synapse. A sensory neuron, nociceptor, is simulated based on multi‐essential synaptic functions. The high recognition accuracy of handwritten digits (>95%) can be achieved, demonstrating that the flexible ionic gel‐gated VO2 Mott transistor opens up alternative approaches to developing highly stable synapses for future flexible neuromorphic systems.