High-Density, Crosstalk-Free, Flexible Electrolyte-Gated Synaptic Transistors Array via All-Photolithography for Multimodal Neuromorphic Computing

IF 19 1区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Li Yuan, Tingting Zhao, Junshuai Dai, Longwei Xue, Xudong Zhang, Cong Peng, Pan Wen, Hai Liu, Hong Hu, Longlong Chen, Hanshen Xin, Jun Li, Xifeng Li, Jianhua Zhang
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Abstract

High-density bio-electrolyte-gated synaptic transistors (BEGTs) array are promising for constructing neuromorphic computing architectures. Due to the bulk ion conductivity and the crack sensitivity of the electrolyte film, patterning the electrolyte is an indispensable route to prevent spatial crosstalk and improve the flexibility of the device array. However, the susceptibility of bio-electrolyte to organic solvents poses challenges in developing reliable all-photolithography techniques for fabricating scalable, patterned, and high-density BEGTs array. This study introduces an all-photolithography method that adopts a photo-crosslinker-enabled electrolyte to create a high-density (11846 devices per cm2) multimodal BEGTs array. This array demonstrates essential neuromorphic behaviors without inter-device crosstalk and maintains its flexibility, enduring 200 bending cycles at a 6 mm radius without significant performance degradation. Meanwhile, the BEGTs array exhibits multimodal synaptic behavior, not only successfully mimicking the biological visual memory system for sensing and processing images but also proving highly accurate in classifying handwritten digits, making it suitable for constructing neuromorphic computing systems. This work offers a dependable strategy for the scalable and stable fabrication of BEGTs array, providing valuable insights for advancing artificial neuromorphic systems.

Abstract Image

Abstract Image

用于多模态神经形态计算的全光刻高密度、无串扰、柔性电解门控突触晶体管阵列
高密度生物电解质门控突触晶体管(BEGTs)阵列是构建神经形态计算体系结构的理想材料。由于电解液薄膜的体离子电导率和裂纹敏感性,对电解液进行图像化是防止空间串扰和提高器件阵列灵活性不可或缺的途径。然而,生物电解质对有机溶剂的敏感性给开发可靠的全光刻技术制造可扩展的、有图案的和高密度的BEGTs阵列带来了挑战。本研究介绍了一种全光刻方法,该方法采用光交联剂激活的电解质来创建高密度(每平方厘米11846个器件)多模态BEGTs阵列。该阵列在没有器件间串扰的情况下表现出基本的神经形态行为,并保持其灵活性,在6mm半径下承受200次弯曲循环而不会显著降低性能。同时,BEGTs阵列表现出多模态突触行为,不仅成功地模仿了生物视觉记忆系统来感知和处理图像,而且在手写体数字分类方面也证明了高度的准确性,使其适合于构建神经形态计算系统。这项工作为BEGTs阵列的可扩展和稳定制造提供了可靠的策略,为推进人工神经形态系统提供了有价值的见解。
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来源期刊
Advanced Functional Materials
Advanced Functional Materials 工程技术-材料科学:综合
CiteScore
29.50
自引率
4.20%
发文量
2086
审稿时长
2.1 months
期刊介绍: Firmly established as a top-tier materials science journal, Advanced Functional Materials reports breakthrough research in all aspects of materials science, including nanotechnology, chemistry, physics, and biology every week. Advanced Functional Materials is known for its rapid and fair peer review, quality content, and high impact, making it the first choice of the international materials science community.
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