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面内极化触发的WS-铁电异质结构突触器件

In-Plane Polarization-Triggered WS-Ferroelectric Heterostructured Synaptic Devices.

作者信息

Qiu Xinxia, Shen Shuwen, Yue Xiaofei, Qin Shoukun, Sheng Chenxu, Xia Dacheng, Huang Xiaoyue, Tian Bobo, Cai Yichen, Qiu Zhi-Jun, Liu Ran, Hu Laigui, Cong Chunxiao

机构信息

School of Information Science and Technology, Fudan University, Shanghai 200433, China.

Key Laboratory of Polar Materials and Devices (MOE), Department of Electronics, East China Normal University, Shanghai 200241, China.

出版信息

ACS Appl Mater Interfaces. 2025 Jan 29;17(4):7027-7035. doi: 10.1021/acsami.4c12111. Epub 2025 Jan 14.

Abstract

To date, various kinds of memristors have been proposed as artificial neurons and synapses for neuromorphic computing to overcome the so-called von Neumann bottleneck in conventional computing architectures. However, related working principles are mostly ascribed to randomly distributed conductive filaments or traps, which usually lead to high stochasticity and poor uniformity. In this work, a heterostructure with a two-dimensional WS monolayer and a ferroelectric PZT film were demonstrated for memristors and artificial synapses, triggered by in-plane ferroelectric polarization. It is noted that the properties of the WS/PZT heterostructures, including photoluminescence (PL) and conductivity, can be effectively tuned by in-plane polarization. In contrast to conventional memristors, the resistance switch of our memristors relies on the dynamic regulation of Schottky barriers at the WS/metal contacts by ferroelectric polarization. PL characterizations verified the existence of lateral fields inside the WS originating from the polarization of the PZT. In particular, such memristors can emulate neuromorphic functions, including threshold-driven spiking, excitatory postsynaptic current, paired-pulse promotion (PPF), and so on. The results indicate that the WS/PZT heterostructures with in-plane polarization are promising for the hardware implementation of artificial neural networks.

摘要

迄今为止,人们已经提出了各种忆阻器作为用于神经形态计算的人工神经元和突触,以克服传统计算架构中所谓的冯·诺依曼瓶颈。然而,相关的工作原理大多归因于随机分布的导电细丝或陷阱,这通常会导致高随机性和差的均匀性。在这项工作中,展示了一种由二维WS单层和铁电PZT薄膜组成的异质结构用于忆阻器和人工突触,其由面内铁电极化触发。值得注意的是,WS/PZT异质结构的性质,包括光致发光(PL)和导电性,可以通过面内极化有效地调节。与传统忆阻器不同,我们的忆阻器的电阻开关依赖于铁电极化对WS/金属接触处肖特基势垒的动态调节。PL表征证实了源自PZT极化的WS内部横向场的存在。特别地,这种忆阻器可以模拟神经形态功能,包括阈值驱动的尖峰、兴奋性突触后电流、双脉冲促进(PPF)等等。结果表明,具有面内极化的WS/PZT异质结构在人工神经网络的硬件实现方面具有前景。

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