Advanced search
Publication Cover
Nanoscale and Microscale Thermophysical Engineering Volume 24, 2020 - Issue 3-4
Submit an article Journal homepage
522
Views
7
CrossRef citations to date
0
Altmetric
Articles

Reconsidering Uncertainty from Frequency Domain Thermoreflectance Measurement and Novel Data Analysis by Deep Learning

Wenqing ShenG.W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, USACorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-1258-3579
ORCID Icon,
Diego VacaG.W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
&
Satish KumarG.W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, USACorrespondence[email protected]
Pages 138-149 | Received 11 Mar 2020, Accepted 03 Aug 2020, Published online: 19 Aug 2020

References

  • D. Zhao, X. Qian, X. Gu, S. A. Jajja, and R. Yang, “Measurement techniques for thermal conductivity and interfacial thermal conductance of bulk and thin film materials,” J. Electron. Packag., vol. 138, no. 4, 2016. DOI:10.1115/1.4034605
  • C. Dames, “Measuring the thermal conductivity of thin films: 3 Omega and related electrothermal methods,” Annual Rev. Heat Trans., vol. 16, no. 1, pp. 7–49, 2013. DOI: 10.1615/AnnualRevHeatTransfer.v16.20.
  • K. T. Regner, S. Majumdar, and J. A. Malen, “Instrumentation of broadband frequency domain thermoreflectance for measuring thermal conductivity accumulation functions,” Rev. Sci. Instrum., vol. 84, no. 6, pp. Art no. 064901, Jun. 2013. DOI: 10.1063/1.4808055.
  • A. J. Schmidt, R. Cheaito, and M. Chiesa, “A frequency-domain thermoreflectance method for the characterization of thermal properties,” Rev. Sci. Instrum., vol. 80, no. 9, pp. 094901, Jan 9. 2009. DOI: 10.1063/1.3212673.
  • J. Yang, C. Maragliano, and A. J. Schmidt, “Thermal property microscopy with frequency domain thermoreflectance,” Rev. Sci. Instrum., vol. 84, no. 10, pp. 104904, 2013. DOI: 10.1063/1.4824143.
  • J. Liu, et al., “Simultaneous measurement of thermal conductivity and heat capacity of bulk and thin film materials using frequency-dependent transient thermoreflectance method,” Rev. Sci. Instrum., vol. 84, no. 3, pp. 034902, 2013. DOI: 10.1063/1.4797479.
  • D. B. Brown, et al., “Spatial mapping of thermal boundary conductance at metal–molybdenum diselenide interfaces,” ACS Appl. Mater. Interfaces, vol. 11, no. 15, pp. 14418–14426, 2019. DOI: 10.1021/acsami.8b22702.
  • T. L. Bougher, et al., “Thermal boundary resistance in GaN films measured by time domain thermoreflectance with robust monte carlo uncertainty estimation,” Nanoscale Microscale Thermophys. Eng., vol. 20, no. 1, pp. 22–32, 2016. DOI: 10.1080/15567265.2016.1154630.
  • Wikipedia. “Levenberg–Marquardt algorithm.” Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title=Levenberg%E2%80%93Marquardt_algorithm&oldid=930437222 ( accessed 2019).
  • Wikipedia. “Trust region.” Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title=Trust_region&oldid=911203117 ( accessed 2019).
  • P. Jiang, X. Qian, and R. Yang, “Tutorial: time-domain thermoreflectance (TDTR) for thermal property characterization of bulk and thin film materials,” J. Appl. Phys., vol. 124, no. 16, pp. 161103, 2018. DOI: 10.1063/1.5046944.
  • J. Yang, E. Ziade, and A. J. Schmidt, “Uncertainty analysis of thermoreflectance measurements,” Rev. Sci. Instrum., vol. 87, no. 1, 2016. DOI:10.1063/1.4939671
  • C. Wei, X. Zheng, D. G. Cahill, and J.-C. Zhao, “Invited article: micron resolution spatially resolved measurement of heat capacity using dual-frequency time-domain thermoreflectance,” Rev. Sci. Instrum., vol. 84, no. 7, pp. 071301, Jan 7. 2013. DOI: 10.1063/1.4815867.
  • L. Liu et al., “Deep learning for generic object detection: a survey,” Int. J. Comput. Vis., vol. 128, no. 2, pp. 261–318, 2019. DOI: 10.1007/s11263-019-01247-4.
  • G. Huang, Z. Liu, L. V. D. Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 21-26 July 2017, pp.2261-2269, doi:10.1109/CVPR35066.2017.
  • T. Young, D. Hazarika, S. Poria, and E. Cambria, “Recent trends in deep learning based natural language processing [Review article],” IEEE Comput. Intell. Mag., vol. 13, no. 3, pp. 55–75, 2018. DOI: 10.1109/mci.2018.2840738.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.