Physical defects reduce the organic solar cells (OSC) functioning. Throughout the OSC fabrication process, the defects can occur, for instance, by scratches or uneven morphologies. In general, bulk defects, interface defects, and interconnect defects can promote shunt and series resistance of the cell. It is crucial to properly detect and classify such defects and their amount in the structure. Correlating such defects with the performance of the cell is important both during the R&D stages to optimize processes, and for mass production stages where defects detection is an integral part of the production line. For the recognition of texture variations in the scanning electron microscope images caused by these defects is crucial the definition of a set of features for texture representation. Because the low‐order Zernike moments can represent the whole shape of the image and the high‐order Zernike moments can describe the detail. Then, in our case, the feature of the image can be represented by a small number of Zernike moments. In fact, the feature set extracted and described by the Zernike moments are not sensitive to the noises and are hardly redundant. So it possible concentrate the signal energy over a set of few vectors. Finally, for classification, an elliptical basis function neural networks was used. The results show effectiveness of the proposed methodology. In fact, we obtained correct classification of 89.3% over testing data set.

中文翻译：

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通过使用新的特征提取算法和具有创新性修剪算法的EBNN，对有机太阳能电池缺陷进行分类

物理缺陷会降低有机太阳能电池（OSC）的功能。在整个OSC制造过程中，缺陷可能会发生，例如，由于划痕或不均匀的形貌。通常，体缺陷，界面缺陷和互连缺陷会促进电池的并联电阻和串联电阻。正确检测和分类此类缺陷及其在结构中的数量至关重要。将这种缺陷与电池的性能相关联对于在优化过程的研发阶段以及对于缺陷检测是生产线必不可少的一部分的大规模生产阶段都是重要的。为了识别由这些缺陷引起的扫描电子显微镜图像中的纹理变化，至关重要的是定义一组用于纹理表示的特征。因为低阶Zernike矩可以代表图像的整体形状，而高阶Zernike矩可以描述细节。然后，在我们的情况下，图像的特征可以由少量的Zernike矩表示。实际上，由Zernike矩提取和描述的特征集对噪声不敏感，并且几乎没有冗余。因此，可以将信号能量集中在少数几个向量上。最后，为了进行分类，使用了椭圆基函数神经网络。结果表明了所提出方法的有效性。实际上，我们在测试数据集上获得了89.3％的正确分类。由Zernike矩提取和描述的特征集对噪声不敏感，并且几乎没有冗余。因此，可以将信号能量集中在少数几个向量上。最后，为了进行分类，使用了椭圆基函数神经网络。结果表明了所提出方法的有效性。实际上，我们在测试数据集上获得了89.3％的正确分类。由Zernike矩提取和描述的特征集对噪声不敏感，并且几乎没有冗余。因此，可以将信号能量集中在少数几个向量上。最后，为了进行分类，使用了椭圆基函数神经网络。结果表明了所提出方法的有效性。实际上，我们在测试数据集上获得了89.3％的正确分类。