首页|Solid-state non-volatile memories based on vdW heterostructure-based vertical-transport ferroelectric field-effect transistors

Solid-state non-volatile memories based on vdW heterostructure-based vertical-transport ferroelectric field-effect transistors

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Driven by the explosive development of data-centric computation applications,it is becoming urgent to develop in-memory computing devices that are beyond the von Neumann architecture with an ar-rangement of separated logic and memory components.The transistor-type solid-state non-volatile memories,such as ferroelectric field-effect transistors(FeFETs),have long been regarded as a competitive candidate for future in-memory computing architectures.However,the density scaling towards high-density arrays would require advanced FeFETs with reduced footprints,which remains a great challenge so far.Here,a vertical-transport(VT)FeFET that flips the charge transport channel perpendicular to the substrate plane is proposed,in which a ferroelectric gate and a van der Waals(vdW)heterojunction channel are vertically integrated,effectively reducing the device footprints.The proposed VT-FeFET shows not only the robust bi-nary non-volatile memory states but also several key synaptic functionalities at the device level.An artificial neural network with supervised learning was simulated based on the device conductance switching properties,showing excellent classification accuracy for the MNIST handwritten digits.These findings suggest that the proposed VT-FeFET could offer a new solution for future non-volatile memories as well as more advanced neuromorphic systems.

vdW heterostructurenon-volatile memoryvertical-transport transistorferroelectric field-effect transistormemristive devices

Qiyu YANG、Zheng-Dong LUO、Fei XIAO、Junpeng ZHANG、Dawei ZHANG、Dongxin TAN、Xuetao GAN、Yan LIU、Zhufei CHU、Yinshui XIA、Genquan HAN

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Hangzhou Institute of Technology,Xidian University,Hangzhou 311200,China

State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology,School of Microelectronics,Xidian University,Xi'an 710071,China

School of Artificial Intelligence,Xidian University,Xi'an 710071,China

School of Materials Science and Engineering,University of New South Wales(UNSW)Sydney,Sydney NSW 2052,Australia

ARC Centre of Excellence in Future Low-Energy Electronics Technologies(FLEET),University of New South Wales(UNSW)Sydney,Sydney NSW 2052,Australia

Key Laboratory of Light Field Manipulation and Information Acquisition,Ministry of Industry and Information Technology,and Shaanxi Key Laboratory of Optical Information Technology,School of Physical Science and Technology,Northwestern Polytechnical University,Xi'an 710129,China

Faculty of Electrical Engineering and Computer Science,Ningbo University,Ningbo 315211,China

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National Key R&D Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaZhejiang Provincial Natural Science Foundation of ChinaZhejiang Provincial Natural Science Foundation of ChinaZhejiang Provincial Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesKey Research and Development Program of Ningbo City

2023YFB4402303620900336202540262274128922642026229352292364204LDT23F04023F04LDT23F04024F04LR21F010003QTZX230792023Z071

2024

10.1007/s11432-024-4004-9

中国科学:信息科学(英文版)
中国科学院

中国科学:信息科学(英文版)

CSTPCDEI
影响因子：0.715
ISSN：1674-733X
年,卷(期)：2024.67(6)