油藏计算
计算机科学
非线性系统
电导
人工神经网络
离子
材料科学
生物系统
光电子学
化学
循环神经网络
物理
人工智能
有机化学
量子力学
生物
凝聚态物理
作者
Zhuohui Liu,Qinghua Zhang,Donggang Xie,Mingzhen Zhang,Xinyan Li,Hai Zhong,Ge Li,Meng He,Dashan Shang,Can Wang,Lin Gu,Ge Yang,Kuijuan Jin,Ge Chen
标识
DOI:10.1038/s41467-023-42993-x
摘要
Abstract Reservoir computing can more efficiently be used to solve time-dependent tasks than conventional feedforward network owing to various advantages, such as easy training and low hardware overhead. Physical reservoirs that contain intrinsic nonlinear dynamic processes could serve as next-generation dynamic computing systems. High-efficiency reservoir systems require nonlinear and dynamic responses to distinguish time-series input data. Herein, an interface-type dynamic transistor gated by an Hf 0.5 Zr 0.5 O 2 (HZO) film was introduced to perform reservoir computing. The channel conductance of Mott material La 0.67 Sr 0.33 MnO 3 (LSMO) can effectively be modulated by taking advantage of the unique coupled property of the polarization process and oxygen migration in hafnium-based ferroelectrics. The large positive value of the oxygen vacancy formation energy and negative value of the oxygen affinity energy resulted in the spontaneous migration of accumulated oxygen ions in the HZO films to the channel, leading to the dynamic relaxation process. The modulation of the channel conductance was found to be closely related to the current state, identified as the origin of the nonlinear response. In the time series recognition and prediction tasks, the proposed reservoir system showed an extremely low decision-making error. This work provides a promising pathway for exploiting dynamic ion systems for high-performance neural network devices.
科研通智能强力驱动
Strongly Powered by AbleSci AI