A support vector machine (SVM) is an important method in the detection and classification of the color difference on a polysilicon wafer. However, the accuracy of a SVM is affected by its feature vector and parameters. Owing to the complex color information and random texture features on the wafer surface, the feature design is extremely complicated. Meanwhile, a SVM optimized using a popular intelligent algorithm easily falls into a local optimum, and the convergence of the algorithm needs to be improved. Therefore, a classification method is proposed for detecting the color difference from multi-scale features in polysilicon wafer images. First, to extract the features, an image segmentation method is devised based on the maximum region contrast, which effectively applies a threshold segmentation of the wafer images. Second, the multi-scale features and color representations in different color spaces are used to construct a nine-dimensional feature vector that sufficiently describes the surface characteristics of the wafer. An approach to optimize the SVM is finally proposed using a magnetic bacteria optimization algorithm based on an elitist strategy for parameter optimization. The optimum individual of each generation is used to adjust the magnetic moment such that the solution approaches the optimal direction and enhances the global search ability. A fitness function is also introduced to improve the diversity of the solutions through a cross-validation method. The experiment results show that the proposed algorithm achieves an accuracy of 98.3% with a better classification performance than the other methods and that the color difference of polysilicon wafers can be effectively detected.

中文翻译：

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基于精英策略的磁性细菌优化算法使用优化支持向量机对多晶硅晶圆进行色差检测

支持向量机（SVM）是多晶硅晶片色差检测和分类的重要方法。然而，SVM 的准确性受其特征向量和参数的影响。由于晶圆表面上复杂的颜色信息和随机的纹理特征，特征设计极其复杂。同时，使用流行的智能算法优化的SVM容易陷入局部最优，算法的收敛性有待提高。因此，提出了一种用于检测多晶硅晶片图像中多尺度特征的色差的分类方法。首先，为了提取特征，设计了一种基于最大区域对比度的图像分割方法，有效地应用了晶圆图像的阈值分割。第二，使用不同颜色空间中的多尺度特征和颜色表示来构建一个九维特征向量，充分描述晶片的表面特征。最后提出了一种基于参数优化的精英策略的磁性细菌优化算法来优化 SVM 的方法。每一代的最优个体用于调整磁矩，使解接近最优方向，增强全局搜索能力。还引入了适应度函数以通过交叉验证方法提高解决方案的多样性。实验结果表明，该算法达到了98的准确率。