School of Science, China University of Geosciences, Beijing 100083, China.
Shenyuan Honor College of Beihang University, Beijing 100191, China.
ACS Appl Mater Interfaces. 2023 Apr 19;15(15):19152-19162. doi: 10.1021/acsami.3c00417. Epub 2023 Apr 6.
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.
具有丰富功能的高性能人工突触器件对于先进类脑神经形态系统的发展至关重要。在这里，我们制备了基于 CVD 生长的 WSe 薄片的突触器件，其具有嵌套三角形的异常形态。WSe 晶体管表现出强大的突触行为，如兴奋性突触后电流、成对脉冲易化、短时间可塑性和长时间可塑性。此外，由于其对光照射具有高灵敏度，WSe 晶体管表现出优异的光剂量依赖性和光波长依赖性可塑性，赋予突触器件更多智能学习和记忆功能。此外，WSe 光电突触可以模拟大脑的“学习经验”行为和联想学习行为。在 MNIST 数据集的手写数字图像模式识别中模拟人工神经网络，基于我们的 WSe 器件的权重更新训练，最佳识别准确率可达 92.9%。详细的表面电势分析和 PL 特性表明，生长过程中产生的本征缺陷主要负责可控的突触可塑性。我们的工作表明，具有能够牢固捕获/释放电荷的本征缺陷的 CVD 生长 WSe 薄片在未来高性能神经形态计算中具有广阔的应用前景。
