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

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npj Computational Materials 2020年第1期6卷 1592-1600页

作者： Zekun Ren Felipe Oviedo Maung Thway Siyu I.P.Tian Yue Wang Hansong Xue Jose Dario Perea mariya layurova Thomas Heumueller Erik Birgersson Armin G.Aberle Christoph J.Brabec Rolf Stangl Qianxiao Li Shijing Sun Fen Lin Ian Marius Peters Tonio Buonassisi Singapore-MIT Alliance for Research and Technology SMART Singapore 138602Singapore Solar Energy Research Institute of Singapore(SERIS) National University of SingaporeSingapore 117574Singapore Massachusetts Institute of Technology CambridgeMA 02139USA Institute of Materials for Electronics and Energy Technology(i-MEET) Friedrich-Alexander University Erlangen-Nürnberg91058 ErlangenGermany Helmholtz Institute HI-ErN Forschungszentrum JülichImmerwahrstrasse 291058 ErlangenGermany National University of Singapore Singapore 119077Singapore

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 ***,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 *** 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 *** 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 *** 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.

关键词： knowledge network enable

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

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npj Computational Materials 2020年第1期6卷 955-955页

作者： Zekun Ren Felipe Oviedo Maung Thway Siyu I.P.Tian Yue Wang Hansong Xue Jose Dario Perea mariya layurova Thomas Heumueller Erik Birgersson Armin G.Aberle Christoph J.Brabec Rolf Stangl Qianxiao Li Shijing Sun Fen Lin Ian Marius Peters Tonio Buonassisi Singapore-MIT Alliance for Research and Technology SMART Singapore Singapore Solar Energy Research Institute of Singapore (SERIS) National University of Singapore Singapore Singapore Massachusetts Institute of Technology Cambridge USA Institute of Materials for Electronics and Energy Technology (i-MEET) Friedrich-Alexander University Erlangen-Nürnberg Erlangen Germany Helmholtz Institute HI-ErN Forschungszentrum Jülich Erlangen Germany National University of Singapore Singapore Singapore

In the original version of the published Article,there was ambiguity in Eq.(1).To improve clarity,Eq.(1)has been corrected to the following.

关键词： enable corrected ambiguity

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