Enhanced dispositioning of reticle defects for advanced masks using virtual stepper with automated defect severity scoring

Pang, Liang; Lu, Alex X.; Chen, Jacky; Guo, Eric; Cai, Lynn; Hung, Che Lun

doi:10.1117/12.518211

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…Other companies offer this method as well (4). This 100% simulation method is not discussed further because it does not provide critical process analysis data, and because little production data is available.…”

Section: Current Defect Analysis Methodsmentioning

confidence: 97%

An automated mask defect analysis system for increasing mask shop productivity

Fiekowsky

Lewis

McDonald³

2005

SPIE Proceedings

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The detection, classification and disposition of defects is an important function that commands significant resources in mask making. Current processes use manual evaluation of defects, which is slow, subject to errors, and provides sparse data for process improvement. The automated defect analysis software described here reads inspection reports from mask inspection tools, classifies each defect, and measures both its size and printability. It combines and compares data from multiple inspections to provide critical process development data. Data from 144 masks is presented showing that the system missed no critical defects found by operators. These inspections also demonstrated numerous occasions for improved classifications compared to that given by the operators. This capability gives improved disposition, an easy path to using simulator based printability for disposition, and significant improvements in mask yield.

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Section: Current Defect Analysis Methodsmentioning

confidence: 97%

An automated mask defect analysis system for increasing mask shop productivity

Fiekowsky

Lewis

McDonald³

2005

SPIE Proceedings

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“…6 (similar to [24]) to estimate the effective W L as shown in (1). Using first-order transistor models, we can then estimate 5 For simplicity and pessimism, we use minimum DR rules instead of using exact design values. the change in drive current caused by this defect, which is then used as a pessimistic approximation of change in cell delay as shown in (2).…”

Section: A Polysilicon Layermentioning

confidence: 99%

“…Defect disposition is done only on the basis on printability that is determined using software tools like Virtual Stepper [5], [6] along with AIMS emulation [8]. It assumes that all printable defects larger than a threshold size (say 10% of mask CD) are critical.…”

Section: B Related Workmentioning

confidence: 99%

“…For all our analysis, we assume that assigning a tolerable defect size of 20%, the minimum width/space design rule is sufficient to prevent shorts or opens. 5 We also assume that a single layout shape is not affected by more than one defect since mask defect density is typically very low (order of few tens of defects for a full reticle). Table I lists the notation used in this section.…”

Section: Criticality Assignmentmentioning

confidence: 99%

“…False, nonprintable, and noncritical defects are filtered out during this step. Images of defects reported by the inspection tool are analyzed using software tools [5], [6] or manually. Often defect images need to be recaptured at a better resolution.…”

Section: A Mask Inspection Primermentioning

confidence: 99%

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Design-Aware Mask Inspection

Kagalwalla

Gupta

Progler

et al. 2012

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Abstract-Mask inspection has become a major bottleneck in the manufacturing flow taking up as much as 40% of the total mask manufacturing time. In this paper, we explore techniques to improve the reticle inspection flow by increasing its design awareness. We develop an algorithm to locate nonfunctional features in a postoptical proximity correction layout without using any design information. Using this, and the timing information of the design (if available), the smallest defect size that could cause the design to fail is assigned to each reticle feature. The criticality of various reticle features is then used to partition the reticle such that each partition is inspected at a different pixel size and sensitivity so that the false and nuisance defect count is reduced without missing any critical defect. We also develop an analytical model to estimate the false and nuisance defect count. Using those models, our simulation results show that this designaware mask inspection can reduce the false and nuisance defect count for a critical polysilicon layer from 80 defects down to 49 defects, leading to substantial reduction in defect review load. We also develop a model to estimate first pass yield (FPY) and show that our method can improve the FPY for a polysilicon layer from 11% to 30%. Apart from the polysilicon layer, the potential benefit of this approach is analyzed for active, contact and all the metal/via layers.Index Terms-Computer-aided design (CAD), design for manufacturability (DFM), mask inspection, mask manufacturing, reticle, semiconductor manufacturing.

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