基于范德华异质结构的超高开关比mem晶体管神经形态计算

IF 14.1 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Wen Deng, Yimeng Yu, Xin Yan, Yifei Li, Lisheng Wang, Jinsong Wu, Jean-Jacques Gaumet, Wen Luo
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引用次数: 0

摘要

忆阻器卓越的电阻开关特性和神经形态的计算潜力对于推进传统和非传统计算范式中的信息处理至关重要。然而,传统的氧化物基记忆电阻器的非理想电阻开关性能难以满足神经形态计算应用的性能要求。此外,双端忆阻器的结构限制了其向多场/多功能调制方向发展。本文提出了一种二维GaSe/MoS2异质结薄膜晶体管,具有四端(4-T)调谐能力和灵活的非易失性存储编程/擦除操作。异质结晶体管具有超过107的特殊电阻开关比,10-106的超宽调制范围,高度可靠的稳定性和循环耐久性。原位开尔文探针力显微镜和动力学表征揭示了缺陷诱导的空间电荷限制介导的传导,以及双栅极终端在通道内调节陷阱态的填充过程。该装置作为一个4-T人工突触,能够实现基本的光电突触操作。基于该装置的人工神经网络所表现出的自去噪和模式识别能力为开发高效节能的神经形态计算架构提供了很好的例子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Ultra-High Switching Ratio Memtransistor Based on Van Der Waals Heterostructures Toward Neuromorphic Computing

Ultra-High Switching Ratio Memtransistor Based on Van Der Waals Heterostructures Toward Neuromorphic Computing

The exceptional resistive switching characteristics and neuromorphic computational potential of memristors are crucial for advancing information processing in both traditional and non-traditional computing paradigms. However, the non-ideal resistive switching behavior of conventional oxide-based memristors hardly meets the performance requirements for neuromorphic computing applications. Besides, the two-terminal memristors are restricted by their configuration limitations toward multi-field/multi-functional modulation. Herein, this article presents a 2D GaSe/MoS2 heterojunction thin-film transistor with four-terminal (4-T) tuning capability and flexible programming/erasing operations for non-volatile storage. The heterojunction transistor demonstrates an exceptional resistance switching ratio exceeding 107, an ultra-wide modulation range of 10–106, highly reliable stability, and cyclic durability. The in situ Kelvin probe force microscope and dynamic characterization reveal the conduction mediated by defect-induced space charge limitations, as well as the tuning filling process of trap states within the channel by dual-gate terminals. This device functions as a 4-T artificial synapse, capable of achieving basic optoelectronic synaptic operations. The self-denoising and pattern recognition capabilities exhibited by artificial neural networks based on this device serve as excellent examples for developing efficient and energy-saving neuromorphic computing architectures.

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来源期刊
Energy & Environmental Materials
Energy & Environmental Materials MATERIALS SCIENCE, MULTIDISCIPLINARY-
CiteScore
17.60
自引率
6.00%
发文量
66
期刊介绍: Energy & Environmental Materials (EEM) is an international journal published by Zhengzhou University in collaboration with John Wiley & Sons, Inc. The journal aims to publish high quality research related to materials for energy harvesting, conversion, storage, and transport, as well as for creating a cleaner environment. EEM welcomes research work of significant general interest that has a high impact on society-relevant technological advances. The scope of the journal is intentionally broad, recognizing the complexity of issues and challenges related to energy and environmental materials. Therefore, interdisciplinary work across basic science and engineering disciplines is particularly encouraged. The areas covered by the journal include, but are not limited to, materials and composites for photovoltaics and photoelectrochemistry, bioprocessing, batteries, fuel cells, supercapacitors, clean air, and devices with multifunctionality. The readership of the journal includes chemical, physical, biological, materials, and environmental scientists and engineers from academia, industry, and policy-making.
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