Automatic vision-based grain optimization and analysis of multi-crystalline solar wafers using hierarchical region growing

• Published in: Engineering optimization Vol. 49; no. 4; pp. 617 - 632
• Main Authors: Fan, Shu-Kai S., Tsai, Du-Ming, Chuang, Wei-Che
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 03.04.2017; Taylor & Francis Ltd
• Subjects: Algorithms; Alloys; Estimates; Grain size; grain size optimization; Grains; image segmentation; multi-crystalline solar wafer; Optimization; Optimization algorithms; Photovoltaic cells; Quality; region growing; Solar cells; Solar energy; Wafers
• ISSN: 0305-215X; 1029-0273
• DOI: 10.1080/0305215X.2016.1206536

Solar power has become an attractive alternative source of energy. The multi-crystalline solar cell has been widely accepted in the market because it has a relatively low manufacturing cost. Multi-crystalline solar wafers with larger grain sizes and fewer grain boundaries are higher quality and convert energy more efficiently than mono-crystalline solar cells. In this article, a new image processing method is proposed for assessing the wafer quality. An adaptive segmentation algorithm based on region growing is developed to separate the closed regions of individual grains. Using the proposed method, the shape and size of each grain in the wafer image can be precisely evaluated. Two measures of average grain size are taken from the literature and modified to estimate the average grain size. The resulting average grain size estimate dictates the quality of the crystalline solar wafers and can be considered a viable quantitative indicator of conversion efficiency.