1. Enhanced Defect Detection in After Develop Inspection with Machine Learning Disposition
- Author
-
Chetan Khatumria, Janice Paduano, Petra Mennell, Gabriel Barber, Justin Zwick, Michael Linnane, Matthew P. McLaughlin, Emerson Benn, Clayton Menser, Robert L. Isaacson, Nathan Hoffman, and Andrew Stamper
- Subjects
business.industry ,Computer science ,education ,Rework ,Process (computing) ,02 engineering and technology ,Disposition ,Image enhancement ,021001 nanoscience & nanotechnology ,Machine learning ,computer.software_genre ,01 natural sciences ,010309 optics ,0103 physical sciences ,Baseline system ,Artificial intelligence ,Sensitivity (control systems) ,Controlled experiment ,0210 nano-technology ,business ,Lithography ,computer - Abstract
A complementary Machine Learning disposition method was generated and tested for after develop inspections in lithography. For lithography coating defects, this new method showed twice the sensitivity and five times the specificity in a controlled experiment versus the baseline system. Applying the detection method along with process improvements, preventative measures and rework for splatter defects, reduced yield loss from splatters by over 30x. Herein we describe learnings on the use of image enhancement for training and disposition, an Explainable AI system to support understanding, and a process flow to train augmentation based on performance.
- Published
- 2021
- Full Text
- View/download PDF