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High-performance artificial synaptic devices with rich functions are highly desired for the development of an advanced brain-like neuromorphic system. Here, we prepare synaptic devices based on a CVD-grown WSe 2 flake, which has an unusual morphology of nested triangles. The WSe 2 transistor exhibits robust synaptic behaviors such as excitatory postsynaptic current, paired-pulse facilitation, short-time plasticity, and long-time plasticity. Furthermore, due to its high sensitivity to light illumination, the WSe 2 transistor exhibits excellent light-dosage-dependent and light wavelength-dependent plasticity, which endow the synaptic device with more intelligent learning and memory functions. In addition, WSe 2 optoelectronic synapses can mimic “learning experience” behavior and associative learning behavior like the brain. An artificial neural network is simulated for pattern recognition of hand-written digital images in the MNIST data set and the best recognition accuracy could reach 92.9% based on weight updating training of our WSe 2 device. Detailed surface potential analysis and PL characterization reveal that the intrinsic defects generated in growth are dominantly responsible for the controllable synaptic plasticity. Our work suggests that the CVD-grown WSe 2 flake with intrinsic defects capable of robust trapping/de-trapping charges holds great application prospects in future high-performance neuromorphic computation.
Guo et al. (Thu,) studied this question.