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用于可重构内存计算的室温制备多级非易失性无铅卤化铯忆阻器

Room-Temperature Fabricated Multilevel Nonvolatile Lead-Free Cesium Halide Memristors for Reconfigurable In-Memory Computing.

作者信息

Su Tsung-Kai, Cheng Wei-Kai, Chen Cheng-Yueh, Wang Wei-Chun, Chuang Yung-Tang, Tan Guang-Hsun, Lin Hao-Cheng, Hou Cheng-Hung, Liu Ching-Min, Chang Ya-Chu, Shyue Jing-Jong, Wu Kai-Chiang, Lin Hao-Wu

机构信息

Department of Materials Science and Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.

Research Center for Applied Science Academia Sinica, Taipei 11529, Taiwan.

出版信息

ACS Nano. 2022 Aug 23;16(8):12979-12990. doi: 10.1021/acsnano.2c05436. Epub 2022 Jul 11.

Abstract

Recently, conductive-bridging memristors based on metal halides, such as halide perovskites, have been demonstrated as promising components for brain-inspired hardware-based neuromorphic computing. However, realizing devices that simultaneously fulfill all of the key merits (low operating voltage, high dynamic range, multilevel nonvolatile storage capability, and good endurance) remains a great challenge. Herein, we describe lead-free cesium halide memristors incorporating a MoO interfacial layer as a type of conductive-bridging memristor. With this design, we obtained highly uniform and reproducible memristors that exhibited all-around resistive switching characteristics: ultralow operating voltages (<0.18 V), low variations (<30 mV), long retention times (>10 s), high endurance (>10, full on/off cycles), record-high on/off ratios (>10, smaller devices having areas <5 × 10 mm), fast switching (<200 ns), and multilevel programming abilities (>64 states). With these memristors, we successfully implemented stateful logic functions in a reconfigurable architecture and accomplished a high classification accuracy ( 90%) in the simulated hand-written-digits classification task, suggesting their versatility in future in-memory computing applications. In addition, we exploited the room-temperature fabrication of the devices to construct a fully functional three-dimensional stack of memristors, which demonstrates their potential of high-density integration desired for data-intensive neuromorphic computing. High-performance, environmentally friendly cesium halide memristors provide opportunities toward next-generation electronics beyond von Neumann architectures.

摘要

最近,基于金属卤化物(如卤化物钙钛矿)的导电桥接忆阻器已被证明是用于基于脑启发硬件的神经形态计算的有前途的组件。然而,实现同时满足所有关键优点(低工作电压、高动态范围、多级非易失性存储能力和良好耐久性)的器件仍然是一个巨大的挑战。在此,我们描述了一种含MoO界面层的无铅卤化铯忆阻器,作为一种导电桥接忆阻器。通过这种设计,我们获得了高度均匀且可重复的忆阻器,其展现出全方位的电阻开关特性:超低工作电压(<0.18 V)、低变化(<30 mV)、长保持时间(>10 s)、高耐久性(>10个完整的开/关循环)、创纪录的高开/关比(>10,面积<5×10 mm的较小器件)、快速开关(<200 ns)以及多级编程能力(>64个状态)。利用这些忆阻器,我们在可重构架构中成功实现了有状态逻辑功能,并在模拟手写数字分类任务中实现了高分类准确率(90%),这表明它们在未来内存计算应用中的多功能性。此外,我们利用器件的室温制造工艺构建了一个功能齐全的三维忆阻器堆栈,这展示了它们对于数据密集型神经形态计算所需的高密度集成的潜力。高性能、环境友好的卤化铯忆阻器为超越冯·诺依曼架构的下一代电子学提供了机遇。

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