ACS Nano|面向神经形态计算和集成视觉信息存储与计算的大规模柔性光突触 ...

文章链接：https://doi.org/10.1021/acsnano.0c08921

摘要

集成了突触和光感应功能的光电突触在神经形态计算中显示出巨大的优势，可用于视觉信息处理以及以节能的方式进行复杂的学习，识别和记忆。然而，电刺激对于现有的光电突触实现双向权重更新仍然是必不可少的，从而限制了设备的处理速度，带宽和集成密度。在本文中，提出了一种基于晶圆级芘基烯二炔/石墨烯/ PbS量子点异质结构的两端光学突触，该突触可模拟光路中的兴奋性和抑制性突触行为。异质结构的简单设备架构和低尺寸特征使光学突触具有可穿戴电子设备的强大灵活性。这种光学突触具有线性和对称的电导更新轨迹，具有许多电导状态和低噪声，这有助于演示准确而有效的模式识别，同时，这种器件即使在弯曲状态也具有很强的容错能力。光学路径中的光学突触已经证明了一系列逻辑功能和联想学习能力，从而大大增强了神经形态计算的信息处理能力。此外，构建了基于光学突触阵列的集成可视信息感测存储器处理系统，以执行实时检测，原位图像存储和区分任务。这项工作是朝着受光遗传学启发的可穿戴电子设备的神经形态计算和自适应并行处理网络发展的重要一步。

Optoelectronic synapses integrating synaptic and optical-sensing functions exhibit large advantages in neuromorphic computing for visual information processing and complex learning, recognition, and memory in an energy-efficient way. However, electric stimulation is still essential for existing optoelectronic synapses to realize bidirectional weight-updating, restricting the processing speed, bandwidth, and integration density of the devices. Herein, a two-terminal optical synapse based on a wafer-scale pyrenyl graphdiyne/graphene/PbS quantum dot heterostructure is proposed that can emulate both the excitatory and inhibitory synaptic behaviors in an optical pathway. The simple device architecture and low-dimensional features of the heterostructure endow the optical synapse with robust flexibility for wearable electronics. This optical synapse features a linear and symmetric conductance-update trajectory with numerous conductance states and low noise, which facilitates the demonstration of accurate and effective pattern recognition with a strong fault-tolerant capability even at bending states. A series of logic functions and associative learning capabilities have been demonstrated by the optical synapses in optical pathways, significantly enhancing the information processing capability for neuromorphic computing. Moreover, an integrated visible information sensing memory processing system based on the optical synapse array is constructed to perform real-time detection, in situ image memorization, and distinction tasks. This work is an important step toward the development of optogenetics-inspired neuromorphic computing and adaptive parallel processing networks for wearable electronics.