Implementation of an efficient defect classification methodology for advanced reticle inspection

Yu, Paul K. L.; Hsu, Vincent; Chen, Ellison; Lai, Rick; Son, Kong; Ma, Weimin; Chang, Peter; Chen, Jackie

doi:10.1117/12.617488

Cited by 2 publications

(3 citation statements)

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“…Introduced through previous work [1], ReviewSmart engine runs in real time concurrently with the defect inspection. When a defect is captured during the inspection, its image is compared to the existing defect images of defects captured before.…”

Section: Reviewsmart Principlementioning

confidence: 99%

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Implementation of an efficient defect classification method in photomask mass production

et al. 2007

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In photomask production environments, increasing productivity of defect inspection and improving fidelity of defect classification are important for mask makers to improve capacity of defect inspection tools and to enhance quality of production. In particular, defect classification time corresponds directly to the cost and the cycle time of mask manufacturing and new product development. KLA-Tencor has introduced an automatic defect grouping tool "ReviewSmart" which automatically bins defects with high fidelity. ReviewSmart has been reported in engineering R&D and evaluation [1], [2]. In this paper, we focus on implementation of ReviewSmart in photomask production. 592 plates were processed during the evaluation period. Those plates are for products of logic, memory and flash. Technology nodes are from 65nm to 180nm. With optimized production setting, the automatic defect grouping tool -ReviewSmart improves productivity of defect inspection by 7% with 100% fidelity. In addition to improve productivity, ReviewSmart is helpful to classify aggressive OPC caused nuisance, troubleshoot process issues and expedite product development and improve usable inspection sensitivity as well.

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Section: Reviewsmart Principlementioning

confidence: 99%

“…ReviewSmart provides an efficient defect classification method by grouping defects according to defect environments and similarity. Previous work on ReviewSmart has reported on how to review and characterize defects accurately and efficiently in engineering and R&D environments [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Implementation of an efficient defect classification method in photomask mass production

et al. 2007

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“…This detector is developed based on the goal of more effective binning of similar defects [3]. ReviewSmart identifies defects which are lithographically similar using a set of operator specified threshold and these similar defects are then binned into group.…”

Section: Reviewsmartmentioning

confidence: 99%

Process window impact of progressive mask defects, its inspection and disposition techniques (go/no-go criteria) via a lithographic detector

et al. 2005

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Progressive mask defect problem is an industry wide mask reliability issue. During the start of this problem when the defects on masks are just forming and are still non-critical, it is possible to continue to run such a problem mask in production with relatively low risk of yield impact. But when the defects approach more critical state, a decision needs to be made whether to pull the mask out of production to send for clean (repair). As this problem increases on the high-end masks running DUV lithography where masks are expensive, it is in the interest of the fab to sustain these problem masks in production as long as possible and take these out of production only when absolutely necessary; i.e., when the defects have reached such a critical condition on these masks that it will impact the process window.During the course of this technical work, investigation has been done towards understanding the impact of such small progressive defects on process window. It was seen that a small growing defect may not print at the best focus exposure condition, but it can still influence the process window and can shrink it significantly.With the help of a high-resolution direct reticle inspection, early detection of these defects is possible, but fabs are still searching for a way to disposition (make a go / no-go decision) on these defective masks. But it is not an easy task as the impact of these defects will depend on not only their size, but also on their transmission and MEEF. A lithographic detector has been evaluated to see if this can predict the criticality of such progressive mask defects.

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Implementation of an efficient defect classification methodology for advanced reticle inspection

Cited by 2 publications

References 0 publications

Implementation of an efficient defect classification method in photomask mass production

Implementation of an efficient defect classification method in photomask mass production

Process window impact of progressive mask defects, its inspection and disposition techniques (go/no-go criteria) via a lithographic detector

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