Artificial Intelligence Applications in Semiconductor Failure Analysis

Abstract

This article provides a systematic overview of knowledge-based and machine-learning AI methods and their potential for use in automated testing, defect identification, fault prediction, root cause analysis, and equipment scheduling. It also discusses the role of decision-making rules, image annotations, and ontologies in automated workflows, data sharing, and interoperability.