We propose a deep learning-based reference comparison system based on a twin network (also known as a Siamese network) for high-performance inspection of integrated circuit (IC) substrates. However, reference comparison-based inspection methods may suffer from false positives when inspecting image pairs with variations, such as mis-registration and color changes. To address these problems, we also propose a novel co-attention module that jointly considers the spatial-wise and channel-wise correlations between a feature block in one image and all other feature blocks in the other image to find similar feature blocks in the other image. By comparing the feature block in one image with similar feature blocks in the other image, the module can reduce the differences in areas where registration errors and/or color variation exist, thereby making the proposed inspection method more robust to image variation than existing methods. We verified the usefulness of the proposed method through experiments using an IC substrate dataset. In the experiments, the proposed method achieved significantly improved performance compared with existing methods in terms of precision and f1-score when the recall is almost the same.

中文翻译：

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用于检查集成电路基板的基于空间和通道共同注意的双网络系统

我们提出了一种基于深度学习的参考比较系统，该系统基于孪生网络（也称为孪生网络），用于集成电路（IC）基板的高性能检查。然而，当检查具有变化的图像对（例如配准错误和颜色变化）时，基于参考比较的检查方法可能会出现误报。为了解决这些问题，我们还提出了一种新颖的共同注意模块，该模块联合考虑一个图像中的特征块与另一幅图像中的所有其他特征块之间的空间和通道相关性，以找到另一幅图像中的相似特征块图像。通过将一个图像中的特征块与另一图像中的相似特征块进行比较，该模块可以减少存在配准错误和/或颜色变化的区域的差异，从而使得所提出的检查方法比现有方法对图像变化更加鲁棒。我们通过使用 IC 基板数据集的实验验证了所提出方法的有用性。实验中，在召回率几乎相同的情况下，与现有方法相比，该方法在精度和 f1 分数方面取得了显着的性能提升。