Tackling data scarcity with transfer learning: a case study of thickness characterization from optical spectra of perovskite thin films

Tackling data scarcity with transfer learning: a case study of thickness characterization from optical spectra of perovskite thin films

Digital Discovery

10.1039/D2DD00149G

http://dx.doi.org/10.1039/d2dd00149g

Siyu Isaac Parker Tian, Zekun Ren, Selvaraj Venkataraj, Yuanhang Cheng, Daniil Bash, Felipe Oviedo, J. Senthilnath, Vijila Chellappan, Yee-Fun Lim, Armin G. Aberle, Benjamin P. MacLeod, Fraser G. L. Parlane, Curtis P. Berlinguette, Qianxiao Li, Tonio Buonassisi, Zhe Liu

13 July 2023

Tian, S. I. P., Ren, Z., Venkataraj, S., Cheng, Y., Bash, D., Oviedo, F., Senthilnath, J., Chellappan, V., Lim, Y.-F., Aberle, A. G., MacLeod, B. P., Parlane, F. G. L., Berlinguette, C. P., Li, Q., Buonassisi, T., & Liu, Z. (2023). Tackling data scarcity with transfer learning: a case study of thickness characterization from optical spectra of perovskite thin films. Digital Discovery, 2(5), 1334–1346. https://doi.org/10.1039/d2dd00149g

thicknessML predicts film thickness from reflection and transmission spectra. Transfer learning enables thickness prediction of different materials with good performance. Transfer learning also bridges the gap between simulation and experiment.

Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

This research / project is supported by the National Research Foundation (NRF) Singapore - Energy Innovation Research Program
Grant Reference no. : NRF2015EWT-EIRP003-004

This research / project is supported by the National Research Foundation (NRF) Singapore - Solar CRP
Grant Reference no. : S18-1176-SCRP

This research / project is supported by the A*STAR - AME Programmatic Fund - Accelerated Materials Development for Manufacturing Program
Grant Reference no. : A1898b0043

This research / project is supported by the National Research Foundation (NRF) Singapore - Competitive Research Grant
Grant Reference no. : NRF-CRP14-2014-03

This research / project is supported by the National Research Foundation, Singapore - NRF fellowship
Grant Reference no. : NRFNRFF13-2021-0005

This research / project is supported by the Solar Energy Research Institute of Singapore (SERIS), a research institute at the National University of Singapore (NUS) supported by the National University of Singapore (NUS), the National Research Foundation Singapore (NRF), the Energy Market Authority of Singapore (EMA), and the Singapore Economic Development Board (EDB).

Financial Support:- 1) Canadian Natural Science and Engineering Research Council (RGPIN-2018-06748) 2) Natural Resources Canada’s Energy Innovation Program (EIP2-MAT-001) for financial support 3) MITei - TOTAL SA research grant

https://oar.a-star.edu.sg/communities-collections/articles/19331

2635-098X

Institute for Infocomm Research

http://dx.doi.org/10.1039/d2dd00149g