Control of Synaptic Plasticity Learning of Ferroelectric Tunnel Memristor by Nanoscale Interface Engineering

• Published in: ACS applied materials & interfaces Vol. 10; no. 15; pp. 12862 - 12869
• Main Authors: Guo, Rui, Zhou, Yaxiong, Wu, Lijun, Wang, Zhuorui, Lim, Zhishiuh, Yan, Xiaobing, Lin, Weinan, Wang, Han, Yoong, Herng Yau, Chen, Shaohai, Ariando, Venkatesan, Thirumalai, Wang, John, Chow, Gan Moog, Gruverman, Alexei, Miao, Xiangshui, Zhu, Yimei, Chen, Jingsheng
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 18.04.2018
• Subjects: Brain; computer analysis; computer hardware; information technology; materials science; Neural Networks (Computer); Neuronal Plasticity; Semiconductors; Synapses; nanoscale interface engineering; synapse; spike-timing-dependent plasticity; ferroelectric tunnel junctions; memristor
• ISSN: 1944-8244; 1944-8252; 1944-8252
• DOI: 10.1021/acsami.8b01469

Brain-inspired computing is an emerging field, which intends to extend the capabilities of information technology beyond digital logic. The progress of the field relies on artificial synaptic devices as the building block for brainlike computing systems. Here, we report an electronic synapse based on a ferroelectric tunnel memristor, where its synaptic plasticity learning property can be controlled by nanoscale interface engineering. The effect of the interface engineering on the device performance was studied. Different memristor interfaces lead to an opposite virgin resistance state of the devices. More importantly, nanoscale interface engineering could tune the intrinsic band alignment of the ferroelectric/metal–semiconductor heterostructure over a large range of 1.28 eV, which eventually results in different memristive and spike-timing-dependent plasticity (STDP) properties of the devices. Bidirectional and unidirectional gradual resistance modulation of the devices could therefore be controlled by tuning the band alignment. This study gives useful insights on tuning device functionalities through nanoscale interface engineering. The diverse STDP forms of the memristors with different interfaces may play different specific roles in various spike neural networks.