AI-Based Analysis of Yield Losses and Defect Detection in Semiconductor Manufacturing

Abstract

Wafer yield serves as a critical process indicator in semiconductor manufacturing systems. With the rapid development of artificial intelligence (AI) technology, using AI to predict and control wafer yields has huge potential and a bright prospect. This paper mainly studies the technologies for utilizing artificial intelligence to enhance semiconductor yield and provides a comprehensive and systematic exposition. It describes current and key technologies and presents opinions and outlooks on future work. This paper explores forms and reasons for defects in wafer yield—functional defect loss and parametric defect loss—and elaborates on corresponding methods for dealing with random defects, system defects, and chip production (particularly memory production). This paper also systematically analyzes the application of artificial intelligence in optimizing semiconductor manufacturing yield, focusing on key technologies such as machine learning-based yield prediction, real-time process control, and defect detection. By comparing the performance of different AI approaches, it proposes an intelligent yield management framework for future semiconductor manufacturing, providing theoretical guidance for industrial intelligent upgrading.