一种具有双功能光电探测器和神经形态视觉光学突触行为的超薄光电忆阻器

IF 5.3 2区 材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Lilan Zou, Junru An, Haonan Xu, Guizhen Wang, Shiwei Lin
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引用次数: 0

摘要

在紧凑的人工视觉应用中,在神经形态装置中集成多种功能对于简化电路设计至关重要。与此同时,人们一直在努力缩小设备的尺寸,以提高集成度。然而,减少有源层的厚度会损害光电性能,影响稳定性、均匀性、耐久性和光敏性。提出了一种具有超薄AlOx/TiOy周期异质结构的光电忆阻器。该设计在不影响光电特性的情况下最大限度地减少了有源层的厚度,并实现了光电探测器、电突触和光学突触在单个器件中的多功能。通过原子层沉积,成功制备了厚度仅为≈12 nm的周期性异质结构。该装置能够实现电突触行为,这对神经形态计算至关重要。值得注意的是,双功能的光电探测器和光学突触有助于在特定的应用场景下有效地获取和处理视觉信息。它使视觉注意力模拟节能目标检测。最后,演示了一个完整的视觉系统,包括传感、前端预处理和后端计算。在此基础上,构建了一个六层卷积神经网络来识别EMNIST模式,并进行了前端预处理,将识别准确率从64%提高到78%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

An Ultrathin Optoelectronic Memristor with Dual-Functional Photodetector and Optical Synapse Behaviors for Neuromorphic Vision

An Ultrathin Optoelectronic Memristor with Dual-Functional Photodetector and Optical Synapse Behaviors for Neuromorphic Vision
Integrating multiple functions within a neuromorphic device is essential for simplifying circuit design in compact artificial vision applications. At the same time, there is a constant push to reduce the size of devices to improve integration. Nevertheless, decreasing the thickness of the active layer compromises photoelectric performance, affecting stability, uniformity, endurance, and photosensitivity. An optoelectronic memristor featuring an ultrathin AlOx/TiOy periodic heterostructure is proposed. This design minimizes the active layer thickness without compromising optoelectronic properties and enables multifunctionality as a photodetector, electric synapse, and optical synapse in a single device. The periodic heterostructure is successfully prepared by atomic layer deposition with a thickness of only ≈12 nm. The device enables electric synaptic behaviors, which are essential for neuromorphic computing. Notably, the dual-functional photodetector and optical synapse facilitate the efficient acquisition and processing of visual information following specific application scenarios. It enables visual attention simulation for energy-efficient object detection. Finally, a complete visual system is demonstrated, encompassing sensing, front-end preprocessing, and back-end computing. Based on the proposed system, a six-layer convolutional neural network is built to recognize EMNIST patterns, and front-end preprocessing improves recognition accuracy from 64% to 78%.
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来源期刊
Advanced Electronic Materials
Advanced Electronic Materials NANOSCIENCE & NANOTECHNOLOGYMATERIALS SCIE-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
11.00
自引率
3.20%
发文量
433
期刊介绍: Advanced Electronic Materials is an interdisciplinary forum for peer-reviewed, high-quality, high-impact research in the fields of materials science, physics, and engineering of electronic and magnetic materials. It includes research on physics and physical properties of electronic and magnetic materials, spintronics, electronics, device physics and engineering, micro- and nano-electromechanical systems, and organic electronics, in addition to fundamental research.
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