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.

In their daily work, engineers in semiconductor Failure Analysis (FA) laboratories generate numerous documents, recording the tasks, findings, and conclusions related to every device they handle. This data stores valuable knowledge for the laboratory that other experts can consult, but being in the form of a collection of documents pertaining to particular devices and their processing history makes it difficult if not practically impossible to find answers to specific questions. This paper therefore proposes a Natural Language Processing (NLP) solution to make the gathering of FA knowledge from numerous documents more efficient. It explains how the authors generated a dataset of FA reports along with corresponding electrical signatures and physical failures in order to train different machine-learning algorithms and compare their performance. Three of the most common classification algorithms were used in the study: K-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Deep Neural Networks (DNN). All of the classification models produced were able to capture patterns associated with different types of failures and predict the causes. The outcomes were best with the SVM classifier and all classifiers did slightly better in regard to physical faults. The reasons are discussed in the paper, which also provides suggestions for future work.

Fault analysis is a complex task that requires engineers to perform various analyses to detect and localize physical defects in semiconductor devices. The process is knowledge intensive and must be precisely documented. In order to ensure unambiguous documentation, engineers must agree on a clearly defined terminology specifying methods, tools, physical faults and their electrical signatures among other things, and it must be stored in a way that is usable for both engineers and software. One possible solution to this challenge is to formalize domain knowledge as an ontology, a knowledge base designed to store terminological definitions. This paper discusses the development of an ontology for electronic device failure analysis that uses a logic-based representation. The latter ensures that terms are interpreted the same way by engineers and software systems, facilitating the automation of tasks such as text classification, information retrieval, and workflow verification.