节能有机光电突触晶体管与环境友好的CuInSe2量子点用于宽带神经形态计算

SmartMat Pub Date : 2023-09-28 DOI:10.1002/smm2.1246
Junyao Zhang, Ziyi Guo, Tongrui Sun, Pu Guo, Xu Liu, Huaiyu Gao, Shilei Dai, Lize Xiong, Jia Huang
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引用次数: 1

摘要

光电突触装置是脑激发高效神经形态计算系统中很有前途的候选元件。然而,由于现有光电突触器件难以实现宽带特性,因此实现宽带神经形态计算具有挑战性。本文利用环境友好型cuins2量子点和有机半导体之间形成的II型异质结构,展示了宽带光电突触晶体管(BPSTs),它可以将从紫外线(UV)到近红外(NIR)的光信号转换为突触后电流。基本的突触功能,如对脉冲促进、记忆水平的调节、长期增强/抑制转换、动态过滤和学习经验行为,都得到了很好的模拟。更重要的是,得益于宽带响应,包括算术计算和模式识别在内的信息处理功能也可以在从紫外到近红外的宽带光谱范围内进行模拟。此外,即使在- 0.1 mV的超低工作电压和75 aJ /事件的超低能耗下,bpst也表现出明显的突触反应,显示出它们在柔性电子领域的潜力。这项研究为未来利用高能效宽带光电器件构建用于高带宽神经形态计算的脑启发神经网络提供了一条途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Energy‐efficient organic photoelectric synaptic transistors with environment‐friendly CuInSe2 quantum dots for broadband neuromorphic computing
Abstract Photoelectric synaptic device is a promising candidate component in brain‐inspired high‐efficiency neuromorphic computing systems. Implementing neuromorphic computing with broad bandwidth is, however, challenging owing to the difficulty in realizing broadband characteristics with available photoelectric synaptic devices. Herein, taking advantage of the type‐II heterostructure formed between environmentally friendly CuInSe 2 quantum dots and organic semiconductor, broadband photoelectric synaptic transistors (BPSTs) that can convert light signals ranging from ultraviolet (UV) to near‐infrared (NIR) into post‐synaptic currents are demonstrated. Essential synaptic functions, such as pair‐pulse facilitation, the modulation of memory level, long‐term potentiation/depression transition, dynamic filtering, and learning‐experience behavior, are well emulated. More significantly, benefitting from broadband responses, information processing functions, including arithmetic computing and pattern recognition can also be simulated in a broadband spectral range from UV to NIR. Furthermore, the BPSTs exhibit obvious synaptic responses even at an ultralow operating voltage of −0.1 mV with an ultralow energy consumption of 75 aJ per event, and show their potential in flexible electronics. This study presents a pathway toward the future construction of brain‐inspired neural networks for high‐bandwidth neuromorphic computing utilizing energy‐efficient broadband photoelectric devices.
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