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全无机CsPbBr钙钛矿平面型忆阻器作为光电突触

All-Inorganic CsPbBr Perovskite Planar-Type Memristors as Optoelectronic Synapses.

作者信息

Liu Zehan, Cheng Pengpeng, Kang Ruyan, Zhou Jian, Wang Xiaoshan, Zhao Xian, Zhao Jia, Zuo Zhiyuan

机构信息

Center for Optics Research and Engineering, Shandong University, Qingdao 266237, P. R. China.

Key Laboratory of Laser & Infrared System (Shandong University), Ministry of Education, Shandong University, Qingdao 266237, P. R. China.

出版信息

ACS Appl Mater Interfaces. 2024 Sep 25;16(38):51065-51079. doi: 10.1021/acsami.4c09673. Epub 2024 Sep 13.

Abstract

Mimicking fundamental synaptic working principles with memristors contributes an essential step toward constructing brain-inspired, high-efficiency neuromorphic systems that surpass von Neumann system computers. Here, an electroforming-free planar-type memristor based on a CsPbBr single crystal is proposed and exhibits excellent resistive switching (RS) behaviors including stable endurance, ultralow power consumption, and fast switching speed. Furthermore, an optically tunable RS performance is demonstrated by manipulating irradiation intensity and wavelength. Optical analysis techniques such as steady-state photoluminescence and time-resolved photoluminescence are employed to investigate the distribution of Br ions and vacancies before and after quantitative polarization, describing migration dynamic processes to elucidate the RS mechanism. Importantly, a CsPbBr single crystal, as the optoelectronic synapse, shows unique potential to emulate photoenhanced synaptic functions such as excitatory postsynaptic current, paired-pulse facilitation, long-term potentiation/depression, spike-timing-dependent plasticity, spike-voltage-dependent plasticity, and learning-forgetting-relearning process with ultralow per synapse event energy consumption. A classical Pavlov's dog experiment is simulated with a combination of optical and electrical stimulation. Finally, pattern recognition with simulated artificial neural networks based on our synapse reached an accuracy of 93.11%. The special strategy and superior RS characteristics of optoelectronic synapses provide a pathway toward high-performance, energy-efficient neuromorphic electronics.

摘要

用忆阻器模仿基本的突触工作原理是构建超越冯·诺依曼系统计算机的受大脑启发的高效神经形态系统的关键一步。在此,提出了一种基于CsPbBr单晶的无电形成平面型忆阻器,它表现出优异的电阻开关(RS)行为,包括稳定的耐久性、超低功耗和快速的开关速度。此外,通过控制辐照强度和波长证明了光可调RS性能。采用稳态光致发光和时间分辨光致发光等光学分析技术研究了定量极化前后Br离子和空位的分布,描述了迁移动力学过程以阐明RS机制。重要的是,CsPbBr单晶作为光电突触,具有独特的潜力来模拟光增强突触功能,如兴奋性突触后电流、双脉冲易化、长时程增强/抑制、尖峰时间依赖可塑性、尖峰电压依赖可塑性以及每个突触事件能耗超低的学习-遗忘-再学习过程。结合光刺激和电刺激模拟了经典的巴甫洛夫犬实验。最后,基于我们的突触用模拟人工神经网络进行模式识别,准确率达到了93.11%。光电突触的特殊策略和优异的RS特性为高性能、节能的神经形态电子学提供了一条途径。

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