Predicting AFM Topography from Optical Microscope Images Using Deep‐Learning-Reference-Cited by-同舟云学术

Predicting AFM Topography from Optical Microscope Images Using Deep‐Learning

Published:2022-12-20 Issue:1 Volume:5 Page:2200317
ISSN:2640-4567
Container-title:Advanced Intelligent Systems
language:en
Short-container-title:Advanced Intelligent Systems

Author:

Jeong Jaewoo¹²,Kim Taeyeong¹²,Lee Bong Jae¹²,Lee Jungchul¹²^ORCID

Affiliation:

1. Department of Mechanical Engineering Korea Advanced Institute of Science and Technology 291, Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea

2. Center for Extreme Thermal Physics and Manufacturing Korea Advanced Institute of Science and Technology 291, Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea

Funder

National Research Foundation of Korea

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aisy.202200317

Reference32 articles.

1. Nanorheology of living cells measured by AFM-based force–distance curves

2. Nanoscale Detection of Ionizing Radiation Damage to DNA by Atomic Force Microscopy

3. Indications of chemical bond contrast in AFM images of a hydrogen-terminated silicon surface

4. Atomic Fingerprinting of Heteroatoms Using Noncontact Atomic Force Microscopy

5. Giant Enhancement of Spin‐Orbit Torque Efficiency in Pt/Co Bilayers by Inserting a WSe 2 under Layer

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