CMP process development based on rapid automatic defect classification

Andrew Skumanich; Man-Ping Cai

doi:10.1117/12.346901

27 April 1999 CMP process development based on rapid automatic defect classification

Andrew Skumanich, Man-Ping Cai

Proceedings Volume 3743, In-Line Characterization, Yield Reliability, and Failure Analyses in Microelectronic Manufacturing; (1999) https://doi.org/10.1117/12.346901
Event: Microelectronic Manufacturing Technologies, 1999, Edinburgh, United Kingdom

Abstract

A critical aspect for the optimization of advanced CMP process development is minimizing defects. Given the complexity of the CMP process, the requirements for defect reduction are increasing. New materials, processes, and consumables lead to unanticipated defect types. In addition, comprehensive defectivity studies must involve patterned wafers since many of the process have defects and issues unique to patterned wafers. A novel defect reduction methodology is described utilizing wafer inspection tools for advanced CMP process development which has been implemented at Applied Materials. This methodology is based on a wafer inspection tools which provide classified defect counts not simply total defect count. A wafer inspection system is used in combination with a high throughput defect- review SEM (SEMVision). This combination of tools provide rapid defect classification and source identification for process development and defect elimination. The WF736 generates 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. Three applications are presented: tool and process development, defect learning for new materials, and process maintenance.

Citation Download Citation

Andrew Skumanich and Man-Ping Cai "CMP process development based on rapid automatic defect classification", Proc. SPIE 3743, In-Line Characterization, Yield Reliability, and Failure Analyses in Microelectronic Manufacturing, (27 April 1999); https://doi.org/10.1117/12.346901

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