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Title: Far-field thermal imaging below diffraction limit

Abstract

Non-uniform self-heating and temperature hotspots are major concerns compromising the performance and reliability of submicron electronic and optoelectronic devices. At deep submicron scales where effects such as contact-related artifacts and diffraction limits accurate measurements of temperature hotspots, non-contact thermal characterization can be extremely valuable. In this work, we use a Bayesian optimization framework with generalized Gaussian Markov random field (GGMRF) prior model to obtain accurate full-field temperature distribution of self-heated metal interconnects from their thermoreflectance thermal images (TRI) with spatial resolution 2.5 times below Rayleigh limit for 530nm illumination. Finite element simulations along with TRI experimental data were used to characterize the point spread function of the optical imaging system. In addition, unlike iterative reconstruction algorithms that use ad hoc regularization parameters in their prior models to obtain the best quality image, we used numerical experiments and finite element modeling to estimate the regularization parameter for solving a real experimental inverse problem.

Authors:
ORCiD logo [1];  [1];  [2];  [3];  [2];  [4];  [2]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Purdue Univ., West Lafayette, IN (United States)
  2. Purdue Univ., West Lafayette, IN (United States)
  3. Purdue Univ., West Lafayette, IN (United States); Univ. of Cincinnati, OH (United States)
  4. Univ. Autónoma de Barcelona, Bellaterra, Catalonia (Spain)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1606852
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Optics Express
Additional Journal Information:
Journal Volume: 28; Journal Issue: 5; Journal ID: ISSN 1094-4087
Publisher:
Optical Society of America (OSA)
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING

Citation Formats

Ziabari, Amirkoushyar, Parsa, Maryam, Xuan, Yi, Bahk, Je-Hyeong, Yazawa, Kazuaki, Alvarez, F. Xavier, and Shakouri, Ali. Far-field thermal imaging below diffraction limit. United States: N. p., 2020. Web. doi:10.1364/OE.380866.
Ziabari, Amirkoushyar, Parsa, Maryam, Xuan, Yi, Bahk, Je-Hyeong, Yazawa, Kazuaki, Alvarez, F. Xavier, & Shakouri, Ali. Far-field thermal imaging below diffraction limit. United States. doi:https://doi.org/10.1364/OE.380866
Ziabari, Amirkoushyar, Parsa, Maryam, Xuan, Yi, Bahk, Je-Hyeong, Yazawa, Kazuaki, Alvarez, F. Xavier, and Shakouri, Ali. Mon . "Far-field thermal imaging below diffraction limit". United States. doi:https://doi.org/10.1364/OE.380866. https://www.osti.gov/servlets/purl/1606852.
@article{osti_1606852,
title = {Far-field thermal imaging below diffraction limit},
author = {Ziabari, Amirkoushyar and Parsa, Maryam and Xuan, Yi and Bahk, Je-Hyeong and Yazawa, Kazuaki and Alvarez, F. Xavier and Shakouri, Ali},
abstractNote = {Non-uniform self-heating and temperature hotspots are major concerns compromising the performance and reliability of submicron electronic and optoelectronic devices. At deep submicron scales where effects such as contact-related artifacts and diffraction limits accurate measurements of temperature hotspots, non-contact thermal characterization can be extremely valuable. In this work, we use a Bayesian optimization framework with generalized Gaussian Markov random field (GGMRF) prior model to obtain accurate full-field temperature distribution of self-heated metal interconnects from their thermoreflectance thermal images (TRI) with spatial resolution 2.5 times below Rayleigh limit for 530nm illumination. Finite element simulations along with TRI experimental data were used to characterize the point spread function of the optical imaging system. In addition, unlike iterative reconstruction algorithms that use ad hoc regularization parameters in their prior models to obtain the best quality image, we used numerical experiments and finite element modeling to estimate the regularization parameter for solving a real experimental inverse problem.},
doi = {10.1364/OE.380866},
journal = {Optics Express},
number = 5,
volume = 28,
place = {United States},
year = {2020},
month = {3}
}

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