Resolution enhancement with machine learning

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131380I.pdf (1.02 MB)

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https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13138/131380I/Resolution-enhancement-with-machine-learning/10.1117/12.3025225.full

Permanent Link

http://hdl.handle.net/11603/35975
 https://doi.org/10.1117/12.3025225

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UMBC Computer Science and Electrical Engineering Department
UMBC Data Science
UMBC Faculty Collection
UMBC Student Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0001-9075-7071

Date

2024-10-03

Type of Work

conference papers and proceedings
video recordings
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Department

Program

Citation of Original Publication

Simsek, Ergun, and Emerson K. Cho. “Resolution Enhancement with Machine Learning.” Applications of Machine Learning 2024 13138 (October 2024): 89–93. https://doi.org/10.1117/12.3025225.

Rights

©2024 Society of Photo-Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.

Subjects

Abstract

This numerical study uses machine learning techniques to enhance the resolution of local near-field probing measurements when the probe is larger than the examined device. The research shows that machine learning can achieve a spatial resolution of λ/10 with a few wavelength-wide probes while keeping the relative error below 3%. It also finds that fully connected neural networks outperform linear regression with limited training data, but linear regression is both sufficient and efficient for larger data sets. These results suggest that similar machine learning methods can improve the resolution of various experimental measurements.