Reticle inspection system using DUV wavelength and new alogorithm platform for advanced reticle inspection for 0.13-μm technology node

Alles, David S.; Terbeek, Paul; Juang, Shauh-Teh; Wiley, James N.; Hsia, Kangmin

doi:10.1117/12.392087

Cited by 3 publications

(1 citation statement)

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“…This results in the tool needing to find classical defects at higher sensitivity as well as new defect types caused by fundamentally new mask manufacturing process. KLA-Tencor STARlight inspection tool is today's industry standard for reticle contamination inspection, detailed descriptions reference to [7][8][9]. In practice, how to keep OPCed small features free from false defects, and there are three ways to cope them, firstly using the advanced algorithm such as the OPC (sub-specification) feature, which is new function implemented in the application software, can handle complex structures.…”

Section: Opc-relative Mask Inspectionmentioning

confidence: 99%

Improving model-based optical proximity correction accuracy using improved process data generation

King

Liang

et al. 2006

SPIE Proceedings

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Model-based Optical Proximity Correction (MBOPC) is used to make systematic modifications to transfer a pattern's design intent from a drawn database to a wafer. This is accomplished by manipulating the shape of mask features to generate the desired pattern (design intent) on the wafer. MBOPC accomplishes this task by dividing drawn patterns into segments, then using a process model to manipulate these segments to achieve the design intent on the wafer. The generation of an accurate process model is very important to the MBOPC process because it contains the process information used to manipulate correction segments. When corrected data are written on a reticle, the faithful and well-controlled reproduction of the data on the mask is critical to realizing the desired lithographic performance. This paper will explore methodologies to improve model accuracy using mask fabrication data and process test patterns. Model accuracy improvement will be accomplished using intelligent sampling plans and representative mask structures. The sampling plan needs to identify critical device and process features. The test mask used to generate the process model needs to have test structures to gather process data. The test mask also must have test structures that can evaluate model quality by testing the extrapolation and interpolation of the model to data that was no used to generate the process model. These methodologies will be shown to improve final mask pattern quality.

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Section: Opc-relative Mask Inspectionmentioning

confidence: 99%