Multicrystalline Solar Wafers Research Articles

Deep learning from imbalanced data for automatic defect detection in multicrystalline solar wafer images

This paper presents a deep learning method for automated defect inspection in multicrystalline solar wafer surfaces. A multicrystalline solar wafer contains local crystal grains with random shapes, sizes and gray-values. It shows a heterogeneous texture in the surface, and makes the automated optical inspection task very challenging. The conventional machine vision needs individually handcraft specific texture features for different types of defects in the silicon solar wafer. The deep learning technique is an end-to-end training. It can learn discriminant features and classification simultaneously. However, it needs a large amount of defect-free and defective samples for the model training. The defect samples are generally not sufficient in the manufacturing process. A generative adversarial network based model is used to generate synthetic defect samples from a small set of real defects. The synthesized defective and true defect-free samples are then used to train a convolutional neural network (CNN) model for defect detection. It overcomes the imbalanced class data problem arising in the CNN model training. The proposed method is compared with the commonly-used learning strategies for imbalanced data. Experimental results show that the proposed deep learning approach can be applied to a variety of defect types found in the multicrystalline solar wafer surface.

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

ABSTRACTSolar 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.

Machine-vision-based identification for wafer tracking in solar cell manufacturing

Solar power is getting popular as an alternative of electricity energy. Multicrystalline solar cells dominate the current market share because of lower material and manufacturing costs. In solar cell manufacturing, a silicon ingot is sliced into thin wafers and then the wafers are further processed into solar cells. Conventional automatic identification systems such as bar codes, magnetic strips, OCR and RFID need a contacted identity on the object surface. They are not possible to implement for solar wafer tracking and data collection due to the thin, fragile silicon surface of a solar wafer. The surface of a multicrystalline solar wafer shows crystal grains of random sizes and shapes and thus forms a unique multi-grain pattern of the surface. In this paper, we propose a machine-vision-based automatic identification approach for shop floor control in solar cell manufacturing. The encoding methods based on wavelet decomposition and wavelet packets in the spectral domain and histogram matching of gradient angles in the spatial domain are proposed to identify solar wafers to their corresponding ingot lots. Experimental results show that wavelet packets have the best discrimination power to encode and identify solar wafers. The gradient angle histogram matching is computationally most efficient, which takes only 0.78 s for a solar wafer of size 2600×2600 pixels.

Wavelet-based defect detection in solar wafer images with inhomogeneous texture

Solar power is an attractive alternative source of electricity. Multicrystalline solar cells dominate the market share owing to their lower manufacturing costs. The surface quality of a solar wafer determines the conversion efficiency of the solar cell. A multicrystalline solar wafer surface contains numerous crystal grains of random shapes and sizes in random positions and directions with different illumination reflections, therefore resulting in an inhomogeneous texture in the sensed image. This texture makes the defect detection task extremely difficult. This paper proposes a wavelet-based discriminant measure for defect inspection in multicrystalline solar wafer images. The traditional wavelet transform techniques for texture analysis and surface inspection rely mainly on the discriminant features extracted in individual decomposition levels. However, these techniques cannot be directly applied to solar wafers with inhomogeneous grain patterns. The defects found in a solar wafer surface generally involve scattering and blurred edges with respect to clear and sharp edges of crystal grains in the background. The proposed method uses the wavelet coefficients in individual decomposition levels as features and the difference of the coefficient values between two consecutive resolution levels as the weights to distinguish local defects from the crystal grain background, and generates a better discriminant measure for identifying various defects in the multicrystalline solar wafers. Experimental results have shown the proposed method performs effectively for detecting fingerprint, contaminant, and saw-mark defects in solar wafer surfaces.

Automatic saw-mark detection in multicrystalline solar wafer images

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.

Mean Shift-Based Defect Detection in Multicrystalline Solar Wafer Surfaces

This paper presents an automated visual inspection scheme for multicrystalline solar wafers using the mean-shift technique. The surface quality of a solar wafer critically determines the conversion efficiency of the solar cell. A multicrystalline solar wafer contains random grain structures and results in a heterogeneous texture in the sensed image, which makes the defect detection task extremely difficult. Mean-shift technique that moves each data point to the mode of the data based on a kernel density estimator is applied for detecting subtle defects in a complicated background. Since the grain edges enclosed in a small spatial window in the solar wafer show more consistent edge directions and a defect region presents a high variation of edge directions, the entropy of gradient directions in a small neighborhood window is initially calculated to convert the gray-level image into an entropy image. The mean-shift smoothing procedure is then performed on the entropy image to remove noise and defect-free grain edges. The preserved edge points in the filtered image can then be easily identified as defective ones by a simple adaptive threshold. Experimental results have shown the proposed method performs effectively for detecting fingerprint and contamination defects in solar wafer surfaces.