Resolution enhancement with machine learning

Files

131380I.pdf (1.02 MB)

Links to Files

https://userpages.cs.umbc.edu/simsek/cps/2024_SPIE_OP_ResEnhancement.pdf

Permanent Link

http://hdl.handle.net/11603/35975

Collections

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

Type of Work

journal articles
postprints
Text

Department

Program

Citation of Original Publication

Rights

This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.

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.