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Single-crystal ferroelectric LiNbO3 thin film-based synaptic devices enabled with tunable domain wall current for neuromorphic computing

神经形态工程学 材料科学 铁电性 光电子学 薄膜 纳米技术 领域(数学分析) 计算机科学 人工神经网络 人工智能 数学 电介质 数学分析
作者
Jiefei Zhu,Changjian Zhou,Qi Liu,Min Zhang
出处
期刊:Applied Physics Letters [American Institute of Physics]
卷期号:125 (19)
标识
DOI:10.1063/5.0219287
摘要

Neuromorphic devices can emulate the human brain to process information, which receives lots of attention in the field of artificial intelligence. Synaptic devices based on ferroelectric thin films feature low-power consumption, multifunctionality, and scalability. Among them, ferroelectric charged domain wall (CDW) devices have attracted intensive interest for the implementation of memristive devices due to their ultrahigh integration ability inherited from the nanoscale domain wall thickness. In particular, the preparation of wafer-scale single-crystalline ferroelectric thin films via ion-sliced heterogeneous wafer bonding lays a good foundation for large-scale integration of ferroelectric devices with functional circuits. However, the biomimic synaptic characteristics and the systematic demonstration of synaptic devices are largely unexplored for this material system. Here, we demonstrate a model synaptic device based on a single-crystal ferroelectric LiNbO3 thin film, which provides the desired characteristics for neuromorphic computing. The conductance modulation demonstrates good linearity for efficient neuromorphic computing applications. Simulations using the Modified National Institute of Standards and Technology handwritten recognition dataset prove that LiNbO3-based synaptic devices can operate with an online learning accuracy of 95.1%. The injection and annihilation of the CDW are proposed as the basis of the conductivity modulation by combining with the piezoresponse force microscopy and conductive atomic force microscopy mapping measurements. With the mature fabrication process of the ultrathin high-quality ferroelectric thin films, LiNbO3-based synaptic devices have an extensive application prospect for future neuromorphic computing systems.
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