downloadGroupGroupnoun_press release_995423_000000 copyGroupnoun_Feed_96767_000000Group 19noun_pictures_1817522_000000Member company iconResource item iconStore item iconGroup 19Group 19noun_Photo_2085192_000000 Copynoun_presentation_2096081_000000Group 19Group Copy 7noun_webinar_692730_000000Path
Skip to main content

Session 8

Defect Inspection

Session Chairs: George Kong, Alexa Greer, Shravan Matham

In-line inspection is critical for modern semiconductor processing. The session talks about defect inspection/review for a wide range of use cases from front to backend of line. This discussion includes application of machine learning to optimize use of inspection tools for defect learnings.

Wednesday, May 3, 2023

12:30 PM ET 
8.1 Utilization of ExtractAI Inspection and Review Methodology to Accelerate Process Development in Advanced Technology Nodes 

Teresa A Esposito, Nathaniel Mowell, Felix Levitov, Applied Materials; Shravan Matham, Monirul Islam, Susan Emans, Rebekah Sheraw, IBM Research 

12:55 
8.2 A Decade of Chasing the Origin of Nano-Whisker/Dendrite Defects from Reliability Qualification into Customer Returns 

Lieyi Sheng, Wei Pan, onsemi 

1:20 
8.3 Combining Full Wafer Inspection with Deep Learning to Recognize Wafers with Critical Defects 

Sabrina Anger, Anil Bora Yayak, Georg Roeder, Martin Schellenberger, Fraunhofer Institute for Integrated Systems and Device Technology IISB; Thomas Alcaire, Delphine Le Cunff, STMicroelectronics; Máté Nagy, Semilab 

1:45 
8.4 Maximizing Efficiency of Inspection tools using SONR 

Rehab Kotb Ali, Nacer Zine El Abidine, Le Hong, Siemens EDA Egypt; Sylvain Olivier Moulis, STMicroelectronics 

Return to ASMC