High-Performance Artificial Synapse Based on CVD-Grown WSe Flakes with Intrinsic Defects.

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 flake, which has an unusual morphology of nested triangles. The WSe 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 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 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 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 flake with intrinsic defects capable of robust trapping/de-trapping charges holds great application prospects in future high-performance neuromorphic computation.