Process and yield improvement based on fast in-line automatic defect classification

Andrew Skumanich

doi:10.1117/12.361342

27 August 1999 Process and yield improvement based on fast in-line automatic defect classification

Andrew Skumanich

Proceedings Volume 3884, In-Line Methods and Monitors for Process and Yield Improvement; (1999) https://doi.org/10.1117/12.361342
Event: Microelectronic Manufacturing '99, 1999, Santa Clara, CA, United States

Abstract

A methodology is presented which dramatically enhances process development and yield improvement by using rapid in-line defect classification. This methodology is based on a wafer inspection tools, both optical and SEM, which provide classified defect counts not simply total defect count. A wafer inspection system (WF736) is used in combination with a high throughput defect-review SEM (SEMVision). This combination of tools provides rapid defect classification and source identification for process development and defect elimination. The WF736 generates defect classification during the inspection with no loss in throughput. The SEMVision allows for further detailed analysis and classification. In addition, patterned wafers are utilized for thorough defect capture and process studies. The methodology provides critical information for improved process development and analysis, as well as enhanced time efficiency. Various applications and cases are presented: tool and process development and in-line monitoring. For each application, the methodology can be applied with slightly different emphasis. In the case of process development, there may be defect learning that requires separate analysis of defects. Here, using smart sampling and the defect review SEM, the exact nature of the defect can then be determined. For process monitoring, when an excursion is detected, the corrective action can be immediately taken.

Citation Download Citation

Andrew Skumanich "Process and yield improvement based on fast in-line automatic defect classification", Proc. SPIE 3884, In-Line Methods and Monitors for Process and Yield Improvement, (27 August 1999); https://doi.org/10.1117/12.361342

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