Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

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...

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Bibliographic Details
Published in:npj computational materials Vol. 6; no. 1
Main Authors: Ren, Zekun, Oviedo, Felipe, Thway, Maung, Tian, Siyu I. P., Wang, Yue, Xue, Hansong, Dario Perea, Jose, Layurova, Mariya, Heumueller, Thomas, Birgersson, Erik, Aberle, Armin G., Brabec, Christoph J., Stangl, Rolf, Li, Qianxiao, Sun, Shijing, Lin, Fen, Peters, Ian Marius, Buonassisi, Tonio
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 31.01.2020
Nature Publishing Group
Subjects:
639/166/987
639/301/1034/1037
639/301/299/946
Arsenic
Arsenides
Bayesian analysis
Characterization and Evaluation of Materials
Chemical vapor deposition
Chemistry
Chemistry and Materials Science
Computational Intelligence
Computational methods
Domains
Electrical and electronic engineering
Embedding
Gallium
Gallium arsenide
Interfacial properties
MATERIALS SCIENCE
Mathematical and Computational Engineering
Mathematical and Computational Physics
Mathematical Modeling and Industrial Mathematics
Neural networks
Optimization
Organic chemicals
Organic chemistry
Photovoltaic cells
Physics
Process variables
Recombination
Solar cells
SOLAR ENERGY
Solvers
Statistical inference
Temperature profiles
Theoretical
ISSN:2057-3960, 2057-3960
Online Access:Get full text
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Summary: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.
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content type line 14
EE0007535
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
ISSN:2057-3960
2057-3960
DOI:10.1038/s41524-020-0277-x