Failure Analysis (FA) is a complex activity that requires careful and complete documentation of all findings and conclusions to preserve knowledge acquired by engineers in this process. Modern FA systems store this data in text or image formats and organize it in databases, file shares, wikis, or other human-readable forms. Given a large volume of generated FA data, navigating it or searching for particular information is hard since machines cannot process the stored knowledge automatically and require much interaction with experts. In this paper, we investigate applications of modern Natural Language Processing (NLP) approaches to the classification of FA texts with respect to electrical and/or physical failures they describe. In particular, we study the efficiency of pretrained Language Models (LM) in the semiconductors domain for text classification with deep neural networks. Evaluation results of LMs show that their vocabulary is not suitable for FA applications, and the best classification accuracy of appr. 60% and 70% for physical and electrical failures, respectively, can only be reached with fine-tuning techniques.

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