Deep learning enables nanoscale X-ray 3D imaging with limited data

Zhao, Chonghang; Yan, Hanfei

doi:10.1038/s41377-023-01198-z

Title: Deep learning enables nanoscale X-ray 3D imaging with limited data

Journal Article · Tue Jun 27 00:00:00 UTC 2023 · Light, Science & Applications

DOI: https://doi.org/10.1038/s41377-023-01198-z · OSTI ID:1993159

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Brookhaven National Laboratory (BNL), Upton, NY (United States). National Synchrotron Light Source II (NSLS-II)

Deep neural network can greatly improve tomography reconstruction with limited data. A recent effort of combining ptycho-tomography model with the 3D U-net demonstrated a significant reduction in both the number of projections and computation time, and showed its potential for integrated circuit imaging that requires high-resolution and fast measurement speed.

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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0012704

OSTI ID:: 1993159

Report Number(s):: BNL-224641-2023-JAAM

Journal Information:: Light, Science & Applications, Journal Name: Light, Science & Applications Journal Issue: 1 Vol. 12; ISSN 2047-7538

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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