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High-performance asymmetric electrode structured light-stimulated synaptic transistor for artificial neural networks
Materials Horizons ( IF 12.2 ) Pub Date : 2023-07-18 , DOI: 10.1039/d3mh00775h Yixin Ran 1 , Wanlong Lu 1 , Xin Wang 1 , Zongze Qin 1 , Xinsu Qin 2 , Guanyu Lu 1 , Zhen Hu 1 , Yuanwei Zhu 1 , Laju Bu 2 , Guanghao Lu 1
Materials Horizons ( IF 12.2 ) Pub Date : 2023-07-18 , DOI: 10.1039/d3mh00775h Yixin Ran 1 , Wanlong Lu 1 , Xin Wang 1 , Zongze Qin 1 , Xinsu Qin 2 , Guanyu Lu 1 , Zhen Hu 1 , Yuanwei Zhu 1 , Laju Bu 2 , Guanghao Lu 1
Affiliation
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Photonics neuromorphic computing shows great prospects due to the advantages of low latency, low power consumption and high bandwidth. Transistors with asymmetric electrode structures are receiving increasing attention due to their low power consumption, high optical response, and simple preparation technology. However, intelligent optical synapses constructed by asymmetric electrodes are still lacking systematic research and mechanism analysis. Herein, we present an asymmetric electrode structure of the light-stimulated synaptic transistor (As-LSST) with a bulk heterojunction as the semiconductor layer. The As-LSST exhibits superior electrical properties, photosensitivity and multiple biological synaptic functions, including excitatory postsynaptic currents, paired-pulse facilitation, and long-term memory. Benefitting from the asymmetric electrode configuration, the devices can operate under a very low drain voltage of 1 × 10−7 V, and achieve an ultra-low energy consumption of 2.14 × 10−18 J per light stimulus event. Subsequently, As-LSST implemented the optical logic function and associative learning. Utilizing As-LSST, an artificial neural network (ANN) with ultra-high recognition rate (over 97.5%) of handwritten numbers was constructed. This work presents an easily-accessible concept for future neuromorphic computing and intelligent electronic devices.
中文翻译：
用于人工神经网络的高性能不对称电极结构光刺激突触晶体管
光子神经形态计算由于具有低延迟、低功耗和高带宽的优势，显示出巨大的前景。具有不对称电极结构的晶体管由于其低功耗、高光学响应和简单的制备技术而受到越来越多的关注。然而，由不对称电极构建的智能光学突触仍缺乏系统的研究和机制分析。在此，我们提出了一种以体异质结作为半导体层的光刺激突触晶体管（As-LSST）的不对称电极结构。As-LSST 表现出优异的电特性、光敏性和多种生物突触功能，包括兴奋性突触后电流、配对脉冲促进和长期记忆。受益于不对称电极配置，该器件可以在1 × 10 -7 V的极低漏极电压下工作，并实现每次光刺激事件2.14 × 10 -18 J的超低能耗。随后，As-LSST实现了光逻辑功能和联想学习。利用As-LSST，构建了手写数字超高识别率（超过97.5%）的人工神经网络（ANN）。这项工作为未来的神经形态计算和智能电子设备提供了一个易于理解的概念。
更新日期：2023-07-18
中文翻译：
用于人工神经网络的高性能不对称电极结构光刺激突触晶体管
光子神经形态计算由于具有低延迟、低功耗和高带宽的优势，显示出巨大的前景。具有不对称电极结构的晶体管由于其低功耗、高光学响应和简单的制备技术而受到越来越多的关注。然而，由不对称电极构建的智能光学突触仍缺乏系统的研究和机制分析。在此，我们提出了一种以体异质结作为半导体层的光刺激突触晶体管（As-LSST）的不对称电极结构。As-LSST 表现出优异的电特性、光敏性和多种生物突触功能，包括兴奋性突触后电流、配对脉冲促进和长期记忆。受益于不对称电极配置，该器件可以在1 × 10 -7 V的极低漏极电压下工作，并实现每次光刺激事件2.14 × 10 -18 J的超低能耗。随后，As-LSST实现了光逻辑功能和联想学习。利用As-LSST，构建了手写数字超高识别率（超过97.5%）的人工神经网络（ANN）。这项工作为未来的神经形态计算和智能电子设备提供了一个易于理解的概念。
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