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

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 

作者机构：Singapore-MIT Alliance for Research and Technology SMARTSingapore 138602Singapore Solar Energy Research Institute of Singapore(SERIS)National University of SingaporeSingapore 117574Singapore Massachusetts Institute of TechnologyCambridgeMA 02139USA Institute of Materials for Electronics and Energy Technology(i-MEET)Friedrich-Alexander University Erlangen-Nürnberg91058 ErlangenGermany Helmholtz Institute HI-ErNForschungszentrum JülichImmerwahrstrasse 291058 ErlangenGermany National University of SingaporeSingapore 119077Singapore 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2020年第6卷第1期

页      面：1592-1600页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research is supported by the National Research Foundation,Prime Minister’s Office,Singapore under its Campus for Research Excellence and Technological Enterprise(CREATE)program and its Energy Innovation Research program EIRP-13(Award No.NRF2015EWT-EIRP003-004)(supporting GaAs device fabrication) by the National Research Foundation(NRF)Singapore through the Singapore Massachusetts Institute of Technology(MIT)Alliance for Research and Technology’s Low Energy Electronic Systems research program(supporting AE and physics-constrained Bayesian inference algorithm development) by the US Department of Energy Photovoltaic Research and Development Program under Award DE-EE0007535(supporting Bayesian optimization algorithm development),and by a TOTAL SA research grant funded through MITei(supporting ML algorithm framing and application) Q.L.acknowledges funding from the Accelerated Materials Development for Manufacturing Program at A*STAR via the AME Programmatic Fund by the Agency for Science,Technology and Research under Grant No.A1898b0043 

主　　题：knowledge network enable 

摘      要：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.