Detection of Failure Analysis Methods with Image Classification

Abstract

Failure analysis (FA) in semiconductors is an error-prone and knowledge-intensive activity. Therefore, timely support of engineers with information about past analyses, best practices, or technical data is crucial for successful FA operations. Unfortunately, in many cases application of modern Artificial Intelligence (AI) methods is limited since most of the data is stored in human-readable formats only, thus, making its automatic processing impossible. In this paper, we consider a problem of method detection from images made by different tools used in FA. We show that the proposed deep learning technique can successfully recognize methods from various images made in an FA lab with an accuracy of 91%. In addition, we investigate the transferability of our results to images of other labs. Obtained results show a slight drop in accuracy to 82%, which can be improved by fine-tuning a model on data from other labs.