The deep learning (DL) model has performed successfully in various fields, including manufacturing. DL models for defect image data analysis in the manufacturing field have been applied to multiple domains such as defect detection, classification, and localization. However, DL models require trade-offs in accuracy and interpretability. We use explainable artificial intelligence techniques to analyze the predicted results of the defect image classification model, which is considered as a “black-box” model, to produce human-understandable results. We visualize defects using layer-wise relevance propagation-based methods, fit the model into a decision tree, and convert prediction results into human-interpretable text. Our research complements the interpretation of prediction results for the classification model. The domain expert can obtain the reliability and explanatory ability for the defect classification of TFT–LCD panel data of the DL model through the results of the proposed analysis.

深度学习（DL）模型已在包括制造在内的各个领域中成功执行。在制造领域中用于缺陷图像数据分析的DL模型已应用于多个领域，例如缺陷检测，分类和定位。但是，DL模型需要在准确性和可解释性之间进行权衡。我们使用可解释的人工智能技术来分析缺陷图像分类模型（被认为是“黑匣子”模型）的预测结果，以产生易于理解的结果。我们使用基于分层相关性传播的方法可视化缺陷，将模型拟合到决策树中，并将预测结果转换为人类可解释的文本。我们的研究对分类模型的预测结果进行了补充。