Skip to main content

Machine learning analysis of broadband optical reflectivity of semiconductor thin film

Abstract

Broadband reflection spectroscopy is a non-invasive and non-contact tool widely used to measure optical dielectric constants and thickness of thin films. However, a lot of time and effort are consumed to analyze data before the results can be attained. Here we construct an artificial neural network (ANN) using scattering matrix formalism and U-net architecture, and apply it to analyze infrared reflection of SiO\(_{2}\) thin film grown on Si substrate. The ANN returns multiple outputs—frequency-dependent optical refractive index (n), absorption coefficient(\(\kappa\)), and thickness of the film (d)—with high precision with 0.6 nm thickness difference. Furthermore, the ANN can fit large number of reflection data taken at numerous positions (500) of the thin film in short time less than 150 ms, and creates fine-scale thickness map with 0.6 nm thickness resolution. This work demonstrates that U-net-based ANN is a powerful method of reflectivity analysis and can be applied to other thin-film materials.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. S.-W. Kim, G.-H. Kim, Appl. Opt. 38, 5968 (1999)

    ADS  Article  Google Scholar 

  2. F. Gao, H. Muhamedsalih, X. Jiang, Opt. Express 20, 21450 (2012)

    ADS  Article  Google Scholar 

  3. M. Brindza, R.A. Flynn, J.S. Shirk, G. Beadie, Opt. Express 22, 28537 (2014)

    ADS  Article  Google Scholar 

  4. P. Nestler, C.A. Helm, Opt. Express 25, 27077 (2017)

    ADS  Article  Google Scholar 

  5. A.B. Kuzmenko, Rev. Sci. Instr. 25, 083108 (2005)

    ADS  Article  Google Scholar 

  6. S.A. Dyakov, V.A. Tolmachev, E.V. Astrova, S.G. Tikhodeev, V.Y. Timoshenko, T.S. Perova, Int. Soc. Opt. Photon. 7521, 75210G (2010)

    Google Scholar 

  7. D.Y.K. Ko, J.C. Inkson, Phys. Rev. B. 38, 9945 (1988)

    ADS  Article  Google Scholar 

  8. J.M. Luque-Raigon, J. Halme, H. Miguez, G. Lozano, J. Opt. 15, 125719 (2013)

    ADS  Article  Google Scholar 

  9. M.F. Tabet, W.A. McGahan, Thin Solid Films 370, 122 (2000)

    ADS  Article  Google Scholar 

  10. M.G. Kim, Int. J. Precis. Eng. Manuf. 21, 219 (2020)

    Article  Google Scholar 

  11. E. Simsek, Mach. Learn. Sci. Technol. 1, 01LT01 (2020)

    Article  Google Scholar 

  12. A. Greco, J. Appl. Crystallogr. 52, 1342 (2019)

    Article  Google Scholar 

  13. R. Houhou et al., Opt. Express 28, 21002 (2020)

    ADS  Article  Google Scholar 

  14. O. Ronneberger, P. Fischer, T. Brox, in Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol. 9351. (Springer, Berlin, 2015), pp. 234–241

  15. Y.M. Kassim et al., in 2019 IEEE International Conference on Image Processing (ICIP), (IEEE, 2019), pp. 1445–1449

  16. Z. Iqbal, D. Nguyen, M.A. Thomas, S. Jiang, Sci. Rep. 11, 1 (2021)

    Article  Google Scholar 

  17. D. F. Edwards, in Handbook of Optical Constants of Solids, edited by E. D. Palik (Academic, New York, 1997)

  18. A. Paszke et al., Advances in Neural Information Processing Systems (Curran Associates Inc, Berlin, 2019), pp. 8024–8035

    Google Scholar 

  19. I. Loshchilov, F. Hutter, arXiv:1608.03983 (2016)

Download references

Acknowledgements

This work was supported by the research foundation of the University of Seoul for EJC (year 2019). K.N.Yu acknowledges HPC Support Project supported by the Ministry of Science and ICT and NIPA.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to E. J. Choi.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing fi-nancial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lee, B., Yu, K., Jeon, J. et al. Machine learning analysis of broadband optical reflectivity of semiconductor thin film. J. Korean Phys. Soc. 80, 347–351 (2022). https://doi.org/10.1007/s40042-022-00436-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40042-022-00436-8

Keywords

  • Optical spectroscopy
  • Machine learning
  • Infrared reflection