High-Performance Artificial Synapse Device Based on Cs3Bi2Br9/NiO Heterostructure for Bio-Inspired Neuromorphic Computing.

IF 8.2 2区材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY

ACS Applied Materials & Interfaces Pub Date : 2025-10-25 DOI:10.1021/acsami.5c14332

Xiuqing Cao,Wenfei Li,Qingqing Zheng,Juan Meng,Leilei Yang,Libin Wang,Yuyang Huang,Shoulei Xu,Wen Deng

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引用次数: 0

Abstract

The development of energy-efficient and biocompatible artificial synapses is essential to advance neuromorphic computing. Bismuth-based perovskites are promising candidates to replace toxic lead-based perovskites in resistive switching devices owing to their exceptional optoelectronic properties, high environmental friendliness, and stability. Here, we present a lead-free Cs3Bi2Br9/NiO heterostructure memristor capable of mimicking biological synaptic functions with exceptional robustness. By engineering a heterostructure with a NiO layer, ion migration in Cs3Bi2Br9 is spatially confined, achieving a resistance switching change rate of less than 7.37% between cycles and enhanced long-term stability in ambient air (60 days). This Cs3Bi2Br9/NiO memristor exhibits excellent stability, impressive memory retention time (>7 × 103 s), durability (>100 cycles), good on/off ratio, and basic synaptic behavior. Furthermore, the training results of 200 data achieved an accuracy rate of 95.46% in the MNIST handwritten digit recognition task, which was superior to traditional analog neural networks. This work not only highlights the significant potential of lead-free perovskites for sustainable neuromorphic hardware but also provides a scalable preparation path for biocompatible electronics.

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基于Cs3Bi2Br9/NiO异质结构的高性能人工突触器件用于仿生神经形态计算。

高效节能和生物相容性的人工突触的发展是推进神经形态计算的必要条件。铋基钙钛矿因其优异的光电性能、高环境友好性和稳定性，在电阻开关器件中有望取代有毒的铅基钙钛矿。在这里，我们提出了一种无铅的Cs3Bi2Br9/NiO异质结构忆阻器，能够模仿生物突触功能，具有出色的鲁棒性。通过设计NiO层异质结构，离子在Cs3Bi2Br9中的迁移受到空间限制，在循环之间实现了小于7.37%的电阻切换变化率，并增强了在环境空气中的长期稳定性（60天）。该Cs3Bi2Br9/NiO忆阻器具有优异的稳定性，令人印象深刻的记忆保持时间（bb7 × 103 s），耐用性（>100周期），良好的通/关比和基本的突触行为。此外，200个数据的训练结果在MNIST手写数字识别任务中达到95.46%的准确率，优于传统的模拟神经网络。这项工作不仅突出了无铅钙钛矿在可持续神经形态硬件方面的巨大潜力，而且为生物相容性电子产品提供了可扩展的制备途径。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Applied Materials & Interfaces 工程技术-材料科学：综合

CiteScore

16.00

自引率

6.30%

发文量

4978

审稿时长

1.8 months

期刊介绍： ACS Applied Materials & Interfaces is a leading interdisciplinary journal that brings together chemists, engineers, physicists, and biologists to explore the development and utilization of newly-discovered materials and interfacial processes for specific applications. Our journal has experienced remarkable growth since its establishment in 2009, both in terms of the number of articles published and the impact of the research showcased. We are proud to foster a truly global community, with the majority of published articles originating from outside the United States, reflecting the rapid growth of applied research worldwide.