Mr. Derek J. Summers Profile

Derek J. Summers

at NXP Semiconductors

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Publications (3)

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Proceedings Article | 11 May 2009 Paper

Automated reticle inspection data analysis for wafer fabs

Derek Summers, Gong Chen, Bryan Reese, Trent Hutchinson, Marcus Liesching, Hai Ying, Russell Dover

Proceedings Volume 7379, 737935 (2009) https://doi.org/10.1117/12.824355

KEYWORDS: Reticles, Inspection, Semiconducting wafers, Data analysis, Defect inspection, Photomasks, Air contamination, Databases, Manufacturing, Error analysis

Read Abstract +

To minimize potential wafer yield loss due to mask defects, most wafer fabs implement some form of reticle inspection system to monitor photomask quality in high-volume wafer manufacturing environments. Traditionally, experienced operators review reticle defects found by an inspection tool and then manually classify each defect as 'pass, warn, or fail' based on its size and location. However, in the event reticle defects are suspected of causing repeating wafer defects on a completed wafer, potential defects on all associated reticles must be manually searched on a layer-by-layer basis in an effort to identify the reticle responsible for the wafer yield loss. This 'problem reticle' search process is a very tedious and time-consuming task and may cause extended manufacturing line-down situations. Often times, Process Engineers and other team members need to manually investigate several reticle inspection reports to determine if yield loss can be tied to a specific layer. Because of the very nature of this detailed work, calculation errors may occur resulting in an incorrect root cause analysis effort. These delays waste valuable resources that could be spent working on other more productive activities. This paper examines an automated software solution for converting KLA-Tencor reticle inspection defect maps into a format compatible with KLA-Tencor's Klarity Defect^(R) data analysis database. The objective is to use the graphical charting capabilities of Klarity Defect to reveal a clearer understanding of defect trends for individual reticle layers or entire mask sets. Automated analysis features include reticle defect count trend analysis and potentially stacking reticle defect maps for signature analysis against wafer inspection defect data. Other possible benefits include optimizing reticle inspection sample plans in an effort to support "lean manufacturing" initiatives for wafer fabs.

Proceedings Article | 24 March 2009 Paper

Automated reticle inspection data analysis for wafer fabs

Derek Summers, Gong Chen, Bryan Reese, Trent Hutchinson, Marcus Liesching, Hai Ying, Russell Dover

Proceedings Volume 7272, 72723P (2009) https://doi.org/10.1117/12.815545

KEYWORDS: Reticles, Inspection, Semiconducting wafers, Data analysis, Defect inspection, Photomasks, Air contamination, Databases, Manufacturing, Error analysis

Read Abstract +

To minimize potential wafer yield loss due to mask defects, most wafer fabs implement some form of reticle inspection system to monitor photomask quality in high-volume wafer manufacturing environments. Traditionally, experienced operators review reticle defects found by an inspection tool and then manually classify each defect as 'pass, warn, or fail' based on its size and location. However, in the event reticle defects are suspected of causing repeating wafer defects on a completed wafer, potential defects on all associated reticles must be manually searched on a layer-by-layer basis in an effort to identify the reticle responsible for the wafer yield loss. This 'problem reticle' search process is a very tedious and time-consuming task and may cause extended manufacturing line-down situations. Often times, Process Engineers and other team members need to manually investigate several reticle inspection reports to determine if yield loss can be tied to a specific layer. Because of the very nature of this detailed work, calculation errors may occur resulting in an incorrect root cause analysis effort. These delays waste valuable resources that could be spent working on other more productive activities. This paper examines an automated software solution for converting KLA-Tencor reticle inspection defect maps into a format compatible with KLA-Tencor's Klarity Defect^TM data analysis database. The objective is to use the graphical charting capabilities of Klarity Defect to reveal a clearer understanding of defect trends for individual reticle layers or entire mask sets. Automated analysis features include reticle defect count trend analysis and potentially stacking reticle defect maps for signature analysis against wafer inspection defect data. Other possible benefits include optimizing reticle inspection sample plans in an effort to support "lean manufacturing" initiatives for wafer fabs.

Proceedings Article | 17 October 2008 Paper

Automated reticle inspection data analysis for wafer fabs

Derek Summers, Gong Chen, Bryan Reese, Trent Hutchinson, Marcus Liesching, Hai Ying, Russell Dover

Proceedings Volume 7122, 71223F (2008) https://doi.org/10.1117/12.801524

KEYWORDS: Reticles, Inspection, Semiconducting wafers, Data analysis, Defect inspection, Photomasks, Air contamination, Databases, Manufacturing, Error analysis

Read Abstract +

To minimize potential wafer yield loss due to mask defects, most wafer fabs implement some form of reticle inspection system to monitor photomask quality in high-volume wafer manufacturing environments. Traditionally, experienced operators review reticle defects found by an inspection tool and then manually classify each defect as 'pass, warn, or fail' based on its size and location. However, in the event reticle defects are suspected of causing repeating wafer defects on a completed wafer, potential defects on all associated reticles must be manually searched on a layer-by-layer basis in an effort to identify the reticle responsible for the wafer yield loss. This 'problem reticle' search process is a very tedious and time-consuming task and may cause extended manufacturing line-down situations. Often times, Process Engineers and other team members need to manually investigate several reticle inspection reports to determine if yield loss can be tied to a specific layer. Because of the very nature of this detailed work, calculation errors may occur resulting in an incorrect root cause analysis effort. These delays waste valuable resources that could be spent working on other more productive activities. This paper examines an automated software solution for converting KLA-Tencor reticle inspection defect maps into a format compatible with KLA-Tencor's Klarity Defec^TM data analysis database. The objective is to use the graphical charting capabilities of Klarity Defect to reveal a clearer understanding of defect trends for individual reticle layers or entire mask sets. Automated analysis features include reticle defect count trend analysis and potentially stacking reticle defect maps for signature analysis against wafer inspection defect data. Other possible benefits include optimizing reticle inspection sample plans in an effort to support "lean manufacturing" initiatives for wafer fabs.

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