Jiejun Wang, Huizhong Zeng, Yiduo Xie, Zebin Zhao, Xinqiang Pan, Wenbo Luo, Yao Shuai, Ling Tang, Dailei Zhu, Qin Xie, Limin Wan, Chuangui Wu, Wanli Zhang

Analog Ion‐Slicing LiNbO3 Memristor Based on Hopping Transport for Neuromorphic Computing

  • General Medicine
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Inspired by human brain, the emerging analog‐type memristor employed in neuromorphic computing systems has attracted tremendous interest. However, existing analog memristors are still far from accurate tuning of multiple conductance states, which are crucial from the device‐level view. Herein, a reliable analog memristor based on ion‐slicing single‐crystalline LiNbO3 (LNO) thin film is demonstrated. The highly ordered LNO crystal structure provides a stable pathway of oxygen vacancy migration, which is contributed to a stable Mott variable‐range hopping process in trap sites. Excellent analog switching characteristics with high reliability and repeatability, including long retention/great endurance with small fluctuation (fluctuated within 0.22%), a large dynamic range of two orders of magnitude, hundreds of distinguishable conductance states with tunable linearity, and ultralow cyclic variances for multiple weight updating (down to 0.75%), are realized with the proposed memristor. As a result, a multilayer perceptron with a high recognition accuracy of 95.6% for Modified National Institute of Standards and Technology dataset is realized. The proposed analog memristive devices based on ion‐slicing single‐crystalline thin films offer a novel strategy for fabricating high‐performance memristors that combined linear tunability and long‐term repeatability, opening a novel avenue for neuromorphic computing application.

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