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Control of Synaptic Plasticity Learning of Ferroelectric Tunnel Memristor by Nanoscale Interface Engineering.
ACS Appl Mater Interfaces. 2018 Apr 18; 10(15):12862-12869.AA

Abstract

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.

Authors+Show Affiliations

Department of Materials Science and Engineering , National University of Singapore , 117575 , Singapore. NUSNNI-Nanocore , National University of Singapore , 117411 , Singapore.Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information , Huazhong University of Science and Technology , Wuhan 430074 , China.Condensed Matter Physics & Materials Science Division, Brookhaven National Laboratory , Upton, New York , New York 11973 , United States.Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information , Huazhong University of Science and Technology , Wuhan 430074 , China.NUSNNI-Nanocore , National University of Singapore , 117411 , Singapore.Department of Materials Science and Engineering , National University of Singapore , 117575 , Singapore.Department of Materials Science and Engineering , National University of Singapore , 117575 , Singapore.Department of Materials Science and Engineering , National University of Singapore , 117575 , Singapore.Department of Materials Science and Engineering , National University of Singapore , 117575 , Singapore.Department of Materials Science and Engineering , National University of Singapore , 117575 , Singapore.NUSNNI-Nanocore , National University of Singapore , 117411 , Singapore. Department of Physics , National University of Singapore , 117542 , Singapore.Department of Materials Science and Engineering , National University of Singapore , 117575 , Singapore. NUSNNI-Nanocore , National University of Singapore , 117411 , Singapore. Department of Physics , National University of Singapore , 117542 , Singapore. Department of Electrical and Computer Engineering , National University of Singapore , 117583 , Singapore.Department of Materials Science and Engineering , National University of Singapore , 117575 , Singapore.Department of Materials Science and Engineering , National University of Singapore , 117575 , Singapore.Department of Physics and Astronomy , University of Nebraska-Lincoln , Lincoln , Nebraska 68588 , United States.Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information , Huazhong University of Science and Technology , Wuhan 430074 , China.Condensed Matter Physics & Materials Science Division, Brookhaven National Laboratory , Upton, New York , New York 11973 , United States.Department of Materials Science and Engineering , National University of Singapore , 117575 , Singapore. NUSNNI-Nanocore , National University of Singapore , 117411 , Singapore.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

29617112

Citation

Guo, Rui, et al. "Control of Synaptic Plasticity Learning of Ferroelectric Tunnel Memristor By Nanoscale Interface Engineering." ACS Applied Materials & Interfaces, vol. 10, no. 15, 2018, pp. 12862-12869.
Guo R, Zhou Y, Wu L, et al. Control of Synaptic Plasticity Learning of Ferroelectric Tunnel Memristor by Nanoscale Interface Engineering. ACS Appl Mater Interfaces. 2018;10(15):12862-12869.
Guo, R., Zhou, Y., Wu, L., Wang, Z., Lim, Z., Yan, X., Lin, W., Wang, H., Yoong, H. Y., Chen, S., Ariando, ., Venkatesan, T., Wang, J., Chow, G. M., Gruverman, A., Miao, X., Zhu, Y., & Chen, J. (2018). Control of Synaptic Plasticity Learning of Ferroelectric Tunnel Memristor by Nanoscale Interface Engineering. ACS Applied Materials & Interfaces, 10(15), 12862-12869. https://doi.org/10.1021/acsami.8b01469
Guo R, et al. Control of Synaptic Plasticity Learning of Ferroelectric Tunnel Memristor By Nanoscale Interface Engineering. ACS Appl Mater Interfaces. 2018 Apr 18;10(15):12862-12869. PubMed PMID: 29617112.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Control of Synaptic Plasticity Learning of Ferroelectric Tunnel Memristor by Nanoscale Interface Engineering. AU - Guo,Rui, AU - Zhou,Yaxiong, AU - Wu,Lijun, AU - Wang,Zhuorui, AU - Lim,Zhishiuh, AU - Yan,Xiaobing, AU - Lin,Weinan, AU - Wang,Han, AU - Yoong,Herng Yau, AU - Chen,Shaohai, AU - Ariando,, AU - Venkatesan,Thirumalai, AU - Wang,John, AU - Chow,Gan Moog, AU - Gruverman,Alexei, AU - Miao,Xiangshui, AU - Zhu,Yimei, AU - Chen,Jingsheng, Y1 - 2018/04/04/ PY - 2018/4/5/pubmed PY - 2019/2/1/medline PY - 2018/4/5/entrez KW - ferroelectric tunnel junctions KW - memristor KW - nanoscale interface engineering KW - spike-timing-dependent plasticity KW - synapse SP - 12862 EP - 12869 JF - ACS applied materials & interfaces JO - ACS Appl Mater Interfaces VL - 10 IS - 15 N2 - 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. SN - 1944-8252 UR - https://www.unboundmedicine.com/medline/citation/29617112/Control_of_Synaptic_Plasticity_Learning_of_Ferroelectric_Tunnel_Memristor_by_Nanoscale_Interface_Engineering_ L2 - https://dx.doi.org/10.1021/acsami.8b01469 DB - PRIME DP - Unbound Medicine ER -