Department of Materials Science and Engineering , National University of Singapore , 117575 , Singapore.
NUSNNI-Nanocore , National University of Singapore , 117411 , Singapore.
ACS Appl Mater Interfaces. 2018 Apr 18;10(15):12862-12869. doi: 10.1021/acsami.8b01469. Epub 2018 Apr 4.
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.
脑启发计算是一个新兴的领域，旨在将信息技术的能力扩展到超越数字逻辑的范围。该领域的进展依赖于人工突触器件作为类脑计算系统的构建块。在这里，我们报告了一种基于铁电隧道忆阻器的电子突触，其突触可塑性学习特性可以通过纳米级界面工程来控制。研究了界面工程对器件性能的影响。不同的忆阻器界面导致器件的初始电阻状态相反。更重要的是，纳米级界面工程可以在 1.28eV 的大范围内调整铁电/金属-半导体异质结构的本征能带排列，从而导致器件的阻变和尖峰时间依赖可塑性（STDP）性能不同。通过调整能带排列，可以控制器件的双向和单向逐渐电阻调制。这项研究为通过纳米级界面工程调整器件功能提供了有用的见解。具有不同界面的忆阻器的不同 STDP 形式可能在各种尖峰神经网络中发挥不同的特定作用。
