Machine learning approach to thickness prediction from in situ spectroscopic ellipsometry data for atomic layer deposition processes. Arunachalam, A., Berriel, S N., Feit, C., Kumar, U., Seal, S., Basu, K., & Banerjee, P. Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films, 40(1):012405, American Vacuum Society, 2022.
bibtex   
@article{arunachalam2022machine,
  title={Machine learning approach to thickness prediction from in situ spectroscopic ellipsometry data for atomic layer deposition processes},
  author={Arunachalam, Ayush and Berriel, S Novia and Feit, Corbin and Kumar, Udit and Seal, Sudipta and Basu, Kanad and Banerjee, Parag},
  journal={Journal of Vacuum Science \& Technology A: Vacuum, Surfaces, and Films},
  volume={40},
  number={1},
  pages={012405},
  year={2022},
  publisher={American Vacuum Society}
}

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