Nano-scale Defect Analysis Through K-Means Clustering of CuInSe2 Solar Cells with Ag and K Incorporation

被引：0

作者：

Nietzold, Tara ^{[1
]}

Stuckelberger, Michael ^{[1
,2
]}

Walker, Trumann ^{[1
]}

Valdes, Nicholas ^{[3
]}

West, Bradley M. ^{[1
,4
]}

Lai, Barry ^{[5
]}

Shafarman, William N. ^{[3
]}

Bertoni, Mariana I. ^{[1
]}

机构：

[1] Arizona State Univ, Tempe, AZ 85281 USA

[2] DESY, D-22607 Hamburg, Germany

[3] Univ Delaware, Newark, DE 19716 USA

[4] Boston Fus Corp, Lexington, MA 02421 USA

[5] Argonne Natl Lab, Adv Photon Source, Argonne, IL 60439 USA

来源：

2019 IEEE 46TH PHOTOVOLTAIC SPECIALISTS CONFERENCE (PVSC) | 2019年

关键词：

CIS; X-ray fluorescence; X-ray beam induced current; K-means; clustering; machine learning;

D O I：

10.1109/pvsc40753.2019.8980709

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Nano-X-ray fluorescence microscopy is used to correlate elemental inhomogeneity to device performance measured through X-ray beam induced current (XBIC). Unsupervised machine learning techniques can be useful in the processing and correlation of XRF/XBIC data. A case study using CuInSe2 solar cells treated with Ag-alloying and KF-post-deposition treatment (PDT) is presented. The implementation of K-means clustering is studied for its ability to identify statistically meaningful results from XRF/XBIC data. The procedure described may be applied to datasets of a similar nature.

引用

页码：2164 / 2168

页数：5

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