利用印刷高透射率 ITO 纤维实现神经形态计算的高灵敏度人工突触

IF 9.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
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引用次数: 0

摘要

人工突触是神经形态电子学的重要组成部分。在这里,我们利用 ITO 纤维作为通道,制造出了固体聚合物电解质门控人工突触(EGAS),它具有 5 mV 的超高灵敏度和超过 3 分钟的长期记忆时间。值得注意的是,数字印刷的 ITO 纤维阵列具有约 99.67% 的超高透射率。利用 EGAS 成功模拟了生物突触可塑性,如兴奋性突触后电流、成对脉冲促进、尖峰频率依赖性可塑性以及突触电位和抑制。根据 EGASs 的突触特性,构建了一个人工神经网络,利用时尚-MNIST 数据集进行监督学习,由于突触的线性和对称可塑性,实现了较高的模式识别率(82.39%)。这项工作为未来神经形态计算的高灵敏度人工突触提供了深入见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

High sensitivity artificial synapses using printed high-transmittance ITO fibers for neuromorphic computing

High sensitivity artificial synapses using printed high-transmittance ITO fibers for neuromorphic computing

Artificial synapses are essential building blocks for neuromorphic electronics. Here, solid polymer electrolyte-gated artificial synapses (EGASs) were fabricated using ITO fibers as channels, which possess an ultra-high sensitivity of 5 mV and a long-term memory time exceeding 3 min. Notably, digitally printed ITO-fiber arrays exhibit an ultra-high transmittance of approximately 99.67 %. Biological synaptic plasticity, such as excitatory postsynaptic current, paired-pulse facilitation, spike frequency-dependent plasticity, and synaptic potentiation and depression, were successfully mimicked using the EGASs. Based on the synaptic properties of the EGASs, an artificial neural network was constructed to perform supervised learning using the Fashion-MNIST dataset, achieving high pattern recognition rate (82.39 %) due to the linear and symmetric synaptic plasticity. This work provides insights into high-sensitivity artificial synapses for future neuromorphic computing.

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来源期刊
Chinese Chemical Letters
Chinese Chemical Letters 化学-化学综合
CiteScore
14.10
自引率
15.40%
发文量
8969
审稿时长
1.6 months
期刊介绍: Chinese Chemical Letters (CCL) (ISSN 1001-8417) was founded in July 1990. The journal publishes preliminary accounts in the whole field of chemistry, including inorganic chemistry, organic chemistry, analytical chemistry, physical chemistry, polymer chemistry, applied chemistry, etc.Chinese Chemical Letters does not accept articles previously published or scheduled to be published. To verify originality, your article may be checked by the originality detection service CrossCheck.
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