基于锌掺杂Ga2O3的双端人工突触在神经形态计算中的应用

IF 7.4 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Huichen Fan  (, ), Haonan Wang  (, ), Wandi Chen  (, ), Wenjuan Su  (, ), Shuchen Weng  (, ), Zhenyou Zou  (, ), Lei Sun  (, ), Xiongtu Zhou  (, ), Chaoxing Wu  (, ), Tailiang Guo  (, ), Yongai Zhang  (, )
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引用次数: 0

摘要

非晶氧化镓(ga - ga2o3)载流子浓度低,迁移率有限,限制了其在神经形态计算中的应用。在本研究中,采用射频磁控溅射(RFMS)技术在无氧条件下制备了掺杂锌的Ga2O3 (ZGO)人工突触器件。与未掺杂Ga2O3相比,ZGO器件在254 nm光照下的兴奋性突触后电流增加了106倍,且响应强度与光脉冲参数呈正相关。在光脉冲调制下,器件表现出从短期可塑性到长期可塑性的动态行为转变,包括对脉冲促进和学习-遗忘-再学习过程。此外,突触事件的电能和光能消耗分别低至28 fJ和2 nJ。机理分析表明,ZGO薄膜中持续的光导效应是由丰富的氧空位引起的。基于ZGO器件的多层感知机仿真在手写数字识别中准确率达到90.74%,在50%噪声条件下仍保持76.18%的准确率。锌掺杂为基于ga2o3的神经形态器件提供了一种新的材料设计方法,展示了未来在神经形态计算中的应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Zn-doped Ga2O3 based two-terminal artificial synapses for neuromorphic computing applications

Amorphous gallium oxide (a-Ga2O3) has a low carrier concentration and limited mobility, which constrains its application in neuromorphic computing. In this study, Zn-doped Ga2O3 (ZGO) artificial synaptic devices were fabricated under oxygen-free conditions using radio-frequency magnetron sputtering (RFMS). Compared to undoped Ga2O3, the ZGO device exhibited a 106-fold increase in excitatory postsynaptic current under 254 nm illumination, with the response intensity positively correlated with the optical pulse parameters. Under light pulse modulation, the devices demonstrated dynamic behavior transitioning from short-term plasticity to long-term plasticity, including paired-pulse facilitation and the learning-forgetting-relearning process. Furthermore, the electrical and optical energy consumption of synaptic events are as low as 28 fJ and 2 nJ, respectively. The mechanism analysis indicates that the persistent photoconductivity effect in the ZGO thin film is attributed to the abundant oxygen vacancies. A multi-layer perceptron simulation based on ZGO devices achieved a 90.74% accuracy in handwritten digit recognition, and maintained 76.18% accuracy even with 50% noise. Zn doping provides a new material design approach for Ga2O3-based neuromorphic devices, demonstrating potential for future applications in neuromorphic computing.

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来源期刊
Science China Materials
Science China Materials Materials Science-General Materials Science
CiteScore
11.40
自引率
7.40%
发文量
949
期刊介绍: Science China Materials (SCM) is a globally peer-reviewed journal that covers all facets of materials science. It is supervised by the Chinese Academy of Sciences and co-sponsored by the Chinese Academy of Sciences and the National Natural Science Foundation of China. The journal is jointly published monthly in both printed and electronic forms by Science China Press and Springer. The aim of SCM is to encourage communication of high-quality, innovative research results at the cutting-edge interface of materials science with chemistry, physics, biology, and engineering. It focuses on breakthroughs from around the world and aims to become a world-leading academic journal for materials science.
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