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超柔性高线性度硅纳米膜突触晶体管阵列

Ultra-Flexible High-Linearity Silicon Nanomembrane Synaptic Transistor Array.

作者信息

Zhu Jiahao, Liu Chen, Gao Ruiyi, Zhang Yuming, Zhang Haonan, Cheng Shiyuan, Liu Dexing, Wang Jialiang, Liu Qi, Wang Zifan, Wang Xinwei, Jin Yufeng, Zhang Min

机构信息

School of Microelectronics and the State Key Laboratory of Wide-Bandgap Semiconductor Devices and Integrated Technology, Xidian University, Xi'an, 710071, China.

School of Electronic and Computer Engineering, Peking University, Shenzhen, 518055, China.

出版信息

Adv Mater. 2025 Feb;37(7):e2413404. doi: 10.1002/adma.202413404. Epub 2025 Jan 2.

Abstract

The increasing demand for mobile artificial intelligence applications has elevated edge computing to a prominent research area. Silicon materials, renowned for their excellent electrical properties, are extensively utilized in traditional electronic devices. However, the development of silicon materials for flexible neuromorphic computing devices encounters great challenges. To address these limitations, ultrasoft silicon nanomembranes have emerged as a focal point due to their capability to preserve the superior electrical properties of silicon while providing substantial mechanical flexibility and interfacial tunability. Despite these advantages, difficulties remain in the transfer process of silicon nanomembranes and their integration for flexible synaptic transistors. In this work, an organic-inorganic hybrid polyimide-AlO dielectric layer has been designed for synaptic behavior grown by an atomic layer deposition process, and integrated with a silicon nanomembrane to realize highly flexible synaptic transistors. These transistors demonstrate stable electrical performance even after undergoing 10 000 bending cycles at an extreme curvature radius of 2.2 mm. Furthermore, the silicon nanomembrane transistors effectively emulate synaptic functions, exhibiting exceptional linearity in their long-term characteristics, making them suitable for the application scenarios of detecting subtle signals. When applied to handwritten digit recognition simulations, these synaptic transistors have achieved a high accuracy rate of 93.2%.

摘要

对移动人工智能应用日益增长的需求已将边缘计算提升为一个重要的研究领域。硅材料以其优异的电学性能而闻名,广泛应用于传统电子设备中。然而,用于柔性神经形态计算设备的硅材料的开发面临巨大挑战。为了解决这些限制,超柔软的硅纳米膜已成为焦点,因为它们能够在保持硅优异电学性能的同时提供显著的机械柔韧性和界面可调性。尽管有这些优点,但硅纳米膜的转移过程及其与柔性突触晶体管的集成仍存在困难。在这项工作中,设计了一种有机-无机杂化聚酰亚胺-AlO介电层,用于通过原子层沉积工艺生长的突触行为,并与硅纳米膜集成以实现高度柔性的突触晶体管。这些晶体管即使在2.2毫米的极端曲率半径下经历10000次弯曲循环后仍表现出稳定的电学性能。此外,硅纳米膜晶体管有效地模拟了突触功能,在其长期特性中表现出出色的线性度,使其适用于检测微弱信号的应用场景。当应用于手写数字识别模拟时,这些突触晶体管实现了93.2%的高精度率。

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