PROCESS MONITORING AND DEFECT DETECTION

Implementing a fully automatic macro defect detection and classification system in a high-production semiconductor fab

[+] Author Affiliations
Juanita G. Miller

Texas Instruments KFAB, 13570 North Central Expressway, Dallas, Texas?75243

Lloyd Lee, Michael Pham

Tokyo Electron America, Inc., 2400 Grove Boulevard, Austin, Texas?78741

David Pham, Manyam Khaja, Kathleen Hennessey

ISOA Incorporated, 1100 West Campbell Road, Richardson, Texas?75080

J. Micro/Nanolith. MEMS MOEMS. 2(1), 76-80 (Jan 01, 2003). doi:10.1117/1.1528947
History: Received Feb. 7, 2002; Revised Sep. 23, 2002; Accepted Oct. 4, 2002; Online January 09, 2003
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The process of shifting from human after develop inspection (ADI) to automatic detection, classification, and review of macro defects in the photolithography process is overviewed. The experiences and improvements from integration of automated macro defect detection and classification systems in a full-volume, state-of-the-art production fab is described. As the semiconductor industry moves toward adoption of integrated metrology solutions, the increased use of commercially available macro automated defect detection and classification systems provide multiple benefits for today’s sub-half micron chip manufacturers. Such systems impact the yield by achieving substantial defect detection, classification accuracy, and reporting consistency. The system not only minimizes the time to detect and to fix manufacturing and processing problems but also simplifies process flow. Identification of process problems is increased considerably by providing 100% detection and review of all wafers in a lot using both stand-alone and inline integrated defect metrology. In order for the system to be utilized with the full capacity, the system has to be set up easily and quickly. Furthermore, programming and optimizing recipes used by the system should be flexible enough to identify for new defect types as they appear or when a different set of classification criteria is needed to focus on specific process problems. These systems are more effectively utilized in the production environment when tool-to-tool matching not only among inline-integrated units, but also between inline integrated and standalone units is available. Defect management methods that help increase process yield at a lower cost are becoming more important than ever in today’s competitive semiconductor market. The most important task in defect management is to identify defects and their possible causes as early as possible. Early detection and diagnosis eliminates engineering time focused on existing bad wafers/dies. Another benefit is to correct the faulty processes to avoid producing additional bad wafers/dies. Early detection of photo problems can significantly increase process yield because the wafers can be reworked. © 2003 Society of Photo-Optical Instrumentation Engineers.

© 2003 Society of Photo-Optical Instrumentation Engineers

Citation

Juanita G. Miller ; Lloyd Lee ; Michael Pham ; David Pham ; Manyam Khaja, et al.
"Implementing a fully automatic macro defect detection and classification system in a high-production semiconductor fab", J. Micro/Nanolith. MEMS MOEMS. 2(1), 76-80 (Jan 01, 2003). ; http://dx.doi.org/10.1117/1.1528947


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