Wang Zijian, Guan Zeyu, Sun Haoyang, Luo Zhen, Zhao Haoyu, Wang He, Yin Yuewei, Li Xiaoguang
Department of Physics and CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei 230026, China.
Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China.
ACS Appl Mater Interfaces. 2022 Jun 1;14(21):24602-24609. doi: 10.1021/acsami.2c04441. Epub 2022 May 23.
Ferroelectric tunnel junction (FTJ) is one promising candidate for next-generation nonvolatile data storage and neural network computing systems. In this work, the high-performance 50 nm-diameter Au/Ti/PbZrTiO (∼3 nm, (111)-oriented)/Nb:SrTiO (Nb: 0.7 wt %) FTJs are achieved to demonstrate the scaling down capability of FTJ. As a nonvolatile memory, the FTJ shows eight distinct resistance states (3 bits) with a large ON/OFF ratio (>10), and these states can be switched at a fast speed of 10 ns. Intriguingly, the long-term potentiation/depression and spike timing-dependent plasticity, that is, fundamental functions of biological synapses, can be emulated in the nanoscale FTJ-based artificial synapse. A convolutional neural network (CNN) simulation is then carried out based on the experimental results, and a high recognition accuracy of ∼93.8% on fashion product images is obtained, which is very close to the result of ∼94.4% by a floating-point-based CNN software. In particular, the FTJ-based CNN simulation also exhibits robustness to input image noises. These results indicate the great potential of FTJ for high-density information storage and neural network computing.
铁电隧道结（FTJ）是下一代非易失性数据存储和神经网络计算系统的一个有前途的候选者。在这项工作中，制备了高性能的直径为50 nm的Au/Ti/PbZrTiO（约3 nm，（111）取向）/Nb:SrTiO（Nb: 0.7 wt%）铁电隧道结，以展示其缩小尺寸的能力。作为一种非易失性存储器，该铁电隧道结表现出八个不同的电阻状态（3比特），具有较大的开/关比（>10），并且这些状态可以在10 ns的快速速度下切换。有趣的是，基于纳米级铁电隧道结的人工突触可以模拟生物突触的长期增强/抑制以及依赖于脉冲时间的可塑性，即生物突触的基本功能。然后基于实验结果进行了卷积神经网络（CNN）模拟，在时尚产品图像上获得了约93.8%的高识别准确率，这与基于浮点的CNN软件得到的约94.4%的结果非常接近。特别是，基于铁电隧道结的CNN模拟对输入图像噪声也表现出鲁棒性。这些结果表明铁电隧道结在高密度信息存储和神经网络计算方面具有巨大潜力。
