Synthetic data augmentation to enhance manual and automated defect detection in microelectronics

Abstract

Failure analysis and defect detection are crucial processes in industries, governments, and societies to mitigate the risks associated with defective microelectronics. The accurate identification of faulty parts is vital for preventing potential damages. However, traditional manual and automated defect detection approaches face challenges due to the scarcity of ground truth data from defective parts. This limitation hampers the effectiveness of subject matter experts and machine learning models in recognizing and classifying new instances of defects. To address this issue, we propose a synthetic data augmentation workflow that generates virtual defective parts, effectively overcoming the data scarcity problem and enabling the creation of large datasets at a low cost. Our approach enhances defect detection capabilities, empowering industries and governments to improve the quality and reliability of electronic devices.