Resolution enhancement with machine learning

Ergun Simsek; Emerson K. Cho

doi:10.1117/12.3025225

3 October 2024 Resolution enhancement with machine learning

Ergun Simsek, Emerson K. Cho

Author Affiliations +

Proceedings Volume 13138, Applications of Machine Learning 2024; 131380I (2024) https://doi.org/10.1117/12.3025225
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Ergun Simsek and Emerson K. Cho "Resolution enhancement with machine learning", Proc. SPIE 13138, Applications of Machine Learning 2024, 131380I (3 October 2024); https://doi.org/10.1117/12.3025225

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE