Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan.
Institute of Materials and System for Sustainability, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan.
Sci Rep. 2019 Jul 10;9(1):10013. doi: 10.1038/s41598-019-46192-x.
Recent developments in artificial intelligence technology has facilitated advances in neuromorphic computing. Electrical elements mimicking the role of synapses are crucial building blocks for neuromorphic computers. Although various types of two-terminal memristive devices have emerged in the mainstream of synaptic devices, a hetero-synaptic artificial synapse, i.e., one with modulatable plasticity induced by multiple connections of synapses, is intriguing. Here, a synaptic device with tunable synapse plasticity is presented that is based on a simple four-terminal rutile TiO single-crystal memristor. In this device, the oxygen vacancy distribution in TiO and the associated bulk carrier conduction can be used to control the resistance of the device. There are two diagonally arranged pairs of electrodes with distinct functions: one for the read/write operation, the other for the gating operation. This arrangement enables precise control of the oxygen vacancy distribution. Microscopic analysis of the Ti valence states in the device reveals the origin of resistance switching phenomena to be an electrically driven redistribution of oxygen vacancies with no changes in crystal structure. Tuning protocols for the write and the gate voltage applications enable high precision control of resistance, or synaptic plasticity, paving the way for the manipulation of learning efficiency through neuromorphic devices.
人工智能技术的最新进展促进了神经形态计算的发展。模拟突触作用的电子元件是神经形态计算机的关键组成部分。尽管各种类型的二端忆阻器已经成为突触器件的主流,但具有由多个突触连接诱导的可调节可塑性的异突触人工突触则更具吸引力。在这里,我们提出了一种基于简单四端金红石 TiO 单晶忆阻器的可调节突触可塑性的突触器件。在该器件中,TiO 中的氧空位分布和相关的体载流子传导可用于控制器件的电阻。有两个对角布置的电极对,具有不同的功能:一个用于读写操作,另一个用于门控操作。这种布置可以精确控制氧空位的分布。对器件中 Ti 价态的微观分析揭示了电阻开关现象的起源是氧空位的电驱动重新分布,而晶体结构没有变化。写入和门控电压应用的调谐协议可实现对电阻或突触可塑性的高精度控制,为通过神经形态器件来操纵学习效率铺平了道路。