Machine Learning Assisted Analysis, Prediction, and Fabrication of High-Efficiency CZTSSe Thin Film Solar Cells

被引:17
|
作者
Karade, Vijay C. [1 ,2 ,3 ]
Sutar, Santosh S. [4 ]
Shin, Seung Wook [5 ]
Suryawanshi, Mahesh P. [6 ]
Jang, Jun Sung [2 ,3 ]
Gour, Kuldeep Singh [1 ,2 ,3 ]
Kamat, Rajanish K. [7 ,8 ]
Yun, Jae Ho [1 ]
Dongale, Tukaram D. [9 ]
Kim, Jin Hyeok [2 ,3 ]
机构
[1] Korea Inst Energy Technol KENTECH, Dept Energy Engn, Naju 522132, South Korea
[2] Chonnam Natl Univ, Optoelect Convergence Res Ctr, Gwangju 61186, South Korea
[3] Chonnam Natl Univ, Dept Mat Sci & Engn, Gwangju 61186, South Korea
[4] Shivaji Univ, Yashwantrao Chavan Sch Rural Dev, Kolhapur 416004, India
[5] Korea Rural Community Corp, Rural Res Inst, Future Agr Res Div, Ansan 15634, South Korea
[6] Univ New South Wales, Sch Photovolta & Renewable Energy Engn, Sydney, NSW 2052, Australia
[7] Shivaji Univ, Dept Elect, Kolhapur 416004, India
[8] Dr Homi Bhabha State Univ, 15 Madam Cama Rd, Mumbai 400032, India
[9] Shivaji Univ, Sch Nanosci & Biotechnol, Computat Elect & Nanosci Res Lab, Kolhapur 416004, India
基金
澳大利亚研究理事会; 新加坡国家研究基金会;
关键词
classification and regression trees; CZTSSe; machine learning; neural networks; random forests; thin film solar cells; PROCESS OPTIMIZATION; PERFORMANCE; ZNO;
D O I
10.1002/adfm.202303459
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The Earth-abundant element-based Cu2ZnSn(S,Se)(4) (CZTSSe) absorber is considered as a promising material for thin-film solar cells (TFSCs). The current record power conversion efficiency (PCE) of CZTSSe TFSCs is approximate to 13%, and it's still lower than CdTe and CIGS-based TFSCs. A further breakthrough in its PCE mainly relies on deep insights into the various device fabrication conditions; accordingly, the experimental-oriented machine learning (ML) approach can be an effective way to discover key governing factors in improving PCE. The present work aims to identify the key governing factors throughout the device fabrication processes and apply them to break the saturated PCE for CZTSSe TFSCs. For realization, over 25,000 data points were broadly collected by fabricating more than 1300 CZTSSe TFSC devices and analyzed them using various ML techniques. Through extensive ML analysis, the i-ZnO thickness is found to be the first, while Zn/Sn compositional ratio and sulfo-selenization temperature are other key governing factors under thin or thick i-ZnO thickness to achieve over 11% PCE. Based on these key governing factors, the applied random forest ML prediction model for PCE showed Adj. R-2 = >0.96. Finally, the best-predicted ML conditions considered for experimental validation showed well-matched experimental outcomes with different ML models.
引用
收藏
页数:11
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