Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics
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s41524-020-0277-x.pdf
Description
Published version
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1.7 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Ren, Zekun
Oviedo, Felipe
Thway, Maung
Tian, Siyu IP
Wang, Yue
Xue, Hansong
Dario Perea, Jose
Layurova, Mariya
Heumueller, Thomas
Birgersson, Erik
Date Issued
2020
Journal
npj Computational Materials
Publisher
Springer Science and Business Media LLC
Citation
Ren, Zekun, Oviedo, Felipe, Thway, Maung, Tian, Siyu IP, Wang, Yue et al. 2020. "Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics." npj Computational Materials, 6 (1).
Version
Final published version
Abstract
© 2020, The Author(s). Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.
MIT Department
Singapore-MIT Alliance in Research and Technology (SMART)
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/S41524-020-0277-X
