Yan Xiaobing
Email Address
mseyanx@nus.edu.sg
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Publication Continuously controllable photoconductance in freestanding BiFeO3 by the macroscopic flexoelectric effect(Nature Research, 2020-12-01) Guo, R; You, L; Lin, W; Abdelsamie, A; Shu, X; Zhou, G; Chen, S; Liu, L; Yan, X; Wang, J; Chen, J; Dr Chen Shaohai; MATERIALS SCIENCE AND ENGINEERING© 2020, The Author(s). Flexoelectricity induced by the strain gradient is attracting much attention due to its potential applications in electronic devices. Here, by combining a tunable flexoelectric effect and the ferroelectric photovoltaic effect, we demonstrate the continuous tunability of photoconductance in BiFeO3 films. The BiFeO3 film epitaxially grown on SrTiO3 is transferred to a flexible substrate by dissolving a sacrificing layer. The tunable flexoelectricity is achieved by bending the flexible substrate which induces a nonuniform lattice distortion in BiFeO3 and thus influences the inversion asymmetry of the film. Multilevel conductance is thus realized through the coupling between flexoelectric and ferroelectric photovoltaic effect in freestanding BiFeO3. The strain gradient induced multilevel photoconductance shows very good reproducibility by bending the flexible BiFeO3 device. This control strategy offers an alternative degree of freedom to tailor the physical properties of flexible devices and thus provides a compelling toolbox for flexible materials in a wide range of applications.Publication A Graphene Oxide Quantum Dots Embedded Charge Trapping Memory with Enhanced Memory Window and Data Retention(Institute of Electrical and Electronics Engineers Inc., 2018) Wang, H.; Yan, X.; Jia, X.; Zhang, Z.; Ho, C.-H.; Lu, C.; Zhang, Y.; Yang, T.; Zhao, J.; Zhou, Z.; Zhao, M.; Ren, D.; MATERIALS SCIENCE AND ENGINEERINGGraphene oxide quantum dots (GOQDs) are integrated with a charge trapping layer in a nonvolatile charge trapping memory. The device structures of Pd/SiO2/ZHO/SiO2/Si and Pd/SiO2/ZHO/GOQDs/SiO2/Si are fabricated, measured, and compared. The GOQD-embedded device demonstrates improved memory window size and data retention characteristics. Under a gate sweeping voltage of ±5 V, the memory window of a GOQD-embedded device is 1.67 V, which is 35.7% larger than the same device without using GOQDs. After a retention time of 1.08 × 104 s, the GOQD-embedded device shows only 1.2% and 3.8% decay in the high-state and low-state capacitances, respectively. The data retention loss of a GOQD-embedded device is reduced by at least 65% when compared to its counterpart, respectively. © 2013 IEEE.Publication Ferroic tunnel junctions and their application in neuromorphic networks(American Institute of Physics Inc., 2020-01-06) Guo, Rui; Lin, Weinan; Yan, Xiaobing; Venkatesan, T.; Chen, Jingsheng; ELECTRICAL AND COMPUTER ENGINEERING; MATERIALS SCIENCE AND ENGINEERINGBrain-inspired neuromorphic computing has been intensively studied due to its potential to address the inherent energy and throughput limitations of conventional Von-Neumann based computing architecture. Memristors are ideal building blocks for artificial synapses, which are the fundamental components of neuromorphic computing. In recent years, the emerging ferroic (ferroelectric and ferromagnetic) tunnel junctions have been shown to be able to function as memristors, which are potential candidates to emulate artificial synapses for neuromorphic computing. Here, we provide a review on the ferroic tunnel junctions and their applications as artificial synapses in neuromorphic networks. We focus on the development history of ferroic tunnel junctions, their physical conduction mechanisms, and the intrinsic dynamics of memristors. Their current applications in neuromorphic networks will also be discussed. Finally, a conclusion and future outlooks on the development of ferroic tunnel junctions will be given. Our goal is to give a broad review of ferroic tunnel junction based artificial synapses that can be applied to neuromorphic computing and to help further ongoing research in this field. © 2020 Author(s).Publication Advances in Memristor-Based Neural Networks(Frontiers Media S.A., 2021-03-24) Xu, W.; Wang, J.; Yan Xiaobing; MATERIALS SCIENCE AND ENGINEERINGThe rapid development of artificial intelligence (AI), big data analytics, cloud computing, and Internet of Things applications expect the emerging memristor devices and their hardware systems to solve massive data calculation with low power consumption and small chip area. This paper provides an overview of memristor device characteristics, models, synapse circuits, and neural network applications, especially for artificial neural networks and spiking neural networks. It also provides research summaries, comparisons, limitations, challenges, and future work opportunities. Copyright © 2021 Xu, Wang and Yan.