Automatic saw-mark detection in multicrystalline solar wafer images

Abstract

This paper presents a method of automatic defect inspection for the photovoltaic industry, with a special focus on multicrystalline solar wafers. It presents a machine vision-based scheme to automatically detect saw-mark defects in solar wafer surfaces. A saw-mark defect is a severe flaw that occurs when a silicon ingot is cut into wafers. Early detection of saw-mark defects in the wafer cutting process can reduce material waste and improve production yields. A multicrystalline solar wafer surface presents random shapes, sizes, and orientations of crystal grains in the surface, making the automatic detection of saw-mark defects extremely difficult. The proposed saw-mark detection scheme involves two main procedures: (1) Fourier image reconstruction to remove the multi-grain background of a solar wafer image and (2) a line detection process in the reconstructed image to locate saw-marks. The Fourier transform (FT) is used to eliminate crystal grain patterns and results in a non-textured surface in the reconstructed image. Since a saw-mark is presented horizontally in the sliced wafer, vertical scan lines in the reconstructed image are individually evaluated by a line detection process. A pixel far away from the line sought can then be effectively identified as a defect point. Experimental results show that the proposed method can effectively detect various saw-mark defects, specifically black lines, white lines, and impurities in multicrystalline solar wafers.