Improved Color Defect Detection With Machine Learning for After Develop Inspections in Lithography

Abstract

Color defect detection was improved with the development of a Machine Learning (ML) method for after develop inspections in lithography. For coating defects, the method exhibited two times the sensitivity and three times the specificity in a trial comparison against the reference method. Using the ML method for disposition along with recipe optimization, predictive maintenance, and rework for coating defects, reduced yield loss from splatters in the long run by over 20x. Herein we describe learnings on the use of image enhancement for training and disposition, an Explainable AI application to support understanding, and a process flow to train augmentation based on performance.