Design-based mask metrology hot spot classification and recipe making through random pattern recognition method

Ying Cui; Kiho Baik; Bob Gleason; Malahat Tavassoli

doi:10.1117/12.690988

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20 October 2006 Design-based mask metrology hot spot classification and recipe making through random pattern recognition method

Ying Cui, Kiho Baik, Bob Gleason, Malahat Tavassoli

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Proceedings Volume 6349, Photomask Technology 2006; 63490O (2006) https://doi.org/10.1117/12.690988
Event: SPIE Photomask Technology, 2006, Monterey, California, United States

Abstract

Design Based Metrology (DBM) requires an integrated process from design to metrology, and the very first and key step of this integration is to translate design CD lists to metrology measurement recipes. Design CD lists can come from different sources, such as design rule check, OPC validation, or yield analysis. These design CD lists can not be directly used to create metrology tool recipes, since tool recipe makers usually require specific information of each CD site, or a measurement matrix. The manual process to identify measurement matrix for each design CD site can be very difficult, especially when the list is in hundreds or more. This paper will address this issue and propose a method to automate Design CD Identification (DCDI), using a new CD Pattern Vector (CDPV) library.

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Ying Cui, Kiho Baik, Bob Gleason, and Malahat Tavassoli "Design-based mask metrology hot spot classification and recipe making through random pattern recognition method", Proc. SPIE 6349, Photomask Technology 2006, 63490O (20 October 2006); https://doi.org/10.1117/12.690988

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