PriMera Scientific Engineering (ISSN: 2834-2550)

Research Article

Volume 4 Issue 1

Semiconductor Wafer Defect Detection using Deep Learning

Rakhi Bhardwaj*, Shreyash Bandi, Ahire Purvesh, Prathamesh aldar and Rushikesh Borse

December 14, 2023

DOI : 10.56831/PSEN-04-097

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Abstract

Accurately detecting and classifying defects in wafers is a crucial aspect of semiconductor manufacturing. This process provides useful insights for identifying the root causes of defects and implementing quality management and yield improvement strategies. The traditional approach to classifying wafer defects involves manual inspection by experienced engineers using computer-aided tools. However, this process can be time-consuming and less accurate. As a result, there has been increasing interest in using deep learning approaches to automate the detection of wafer defects, which can improve the accuracy of the detection process.

Keywords: Wafer detection; Deep learning; Object detection; Classification

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