Automatic surface defect inspection in multicrystalline solar wafers

• Main Authors: Wei-Chen Li, 李韋辰
• Other Authors: Du-MingTsai
• Format: Others
• Language: en_US
• Online Access: http://ndltd.ncl.edu.tw/handle/57848964887405788938

博士 === 元智大學 === 工業工程與管理學系 === 100 === Solar power is an attractive alternative source of electricity. Multicrystalline solar cells dominate the market share owing to their lower manufacturing and material costs. The surface quality of solar wafer critically determines the conversion efficiency of the solar cell. In this research, three surface defect inspection techniques are presented for identifying low-contrast defects in non-textured surfaces (for backside solar cells) and detecting small local defects in inhomogeneous surfaces (for solar wafers). The first two solar cells/wafers surface inspection algorithms use a Hough-like line detection method to identify defect points on 1D gray-level profiles of scan lines in the image. The conventional Hough transform requires a sufficient number of points lying exactly on the same straight line at a given parameter resolution so that the accumulator will show a distinct peak in the parameter space. It fails to detect a line in a non-stationary signal. The first proposed Hough-like algorithm can effectively detect the low-contrast defects in the unevenly-illuminated surface of a backside solar cell. In the second proposed method, the inhomogeneous background of multicrystalline grains in a solar wafer image can be effectively removed by properly selecting the band-rejection region in the Fourier spectrum, and then the proposed Hough-like line detection technique is used to identify saw-mark defects in a solar wafer. The third surface defect inspection method is based on the two-dimensional discrete wavelet transform, and is applied to the detection of various defect types. It takes the energy difference between two consecutive decomposition levels as a clue to enhance the discriminant features extracted in individual decomposition levels and generates a better discriminant measure for identifying defects with scattering and blurred edges. Experimental results have shown that the proposed methods perform effectively for detecting low-contrast bump in the unevenly-illuminated backside solar cell, and various defects of stain, saw-mark, fingerprint and contaminant in the inhomogeneous solar wafer surface