High-Speed Piezoresponse Force Microscopy and Machine Learning Approaches for Dynamic Domain Growth in Ferroelectric Materials-Reference-Cited by-同舟云学术

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High-Speed Piezoresponse Force Microscopy and Machine Learning Approaches for Dynamic Domain Growth in Ferroelectric Materials

Published:2020-02-03 Issue:8 Volume:12 Page:9944-9952
ISSN:1944-8244
Container-title:ACS Applied Materials & Interfaces
language:en
Short-container-title:ACS Appl. Mater. Interfaces

Author:

Liu Yue¹,Yu Bingxue¹,Liu Zhiwen²,Beck David³,Zeng Kaiyang¹^ORCID

Affiliation:

1. Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117576

2. Oxford Instruments, Building B2 west, No. 11, West Third Ring North Road, Haidian District,Beijing 100089, P. R. China

3. Oxford Instruments, Asylum Research, Inc., 6310 Hollister Ave, Santa Barbara, California 93117, United States

Funder

Ministry of Education - Singapore

National University of Singapore

Publisher

American Chemical Society (ACS)

Subject

General Materials Science

Link

https://pubs.acs.org/doi/pdf/10.1021/acsami.9b21306

Reference54 articles.

1. Applications of Modern Ferroelectrics

2. Ferroelectric Memories

3. Intrinsic single-domain switching in ferroelectric materials on a nearly ideal surface

4. Domain Formation and Domain Wall Motions in Ferroelectric BaTiO3Single Crystals

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3. Deep learning for exploring ultra-thin ferroelectrics with highly improved sensitivity of piezoresponse force microscopy;npj Computational Materials;2023-02-28

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