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

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Tackling data scarcity with transfer learning: a case study of thickness characterization from optical spectra of perovskite thin films
Title:
Tackling data scarcity with transfer learning: a case study of thickness characterization from optical spectra of perovskite thin films
Journal Title:
Digital Discovery
Keywords:
Publication Date:
13 July 2023
Citation:
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
Abstract:
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.
License type:
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Funding Info:
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
Description:
ISSN:
2635-098X