High speed mask inspection data prep flow based on pipelining

Dan Hung; Domingo Morales; Juan Pablo Canepa; Stephen Kim; Po Liu; Jean-Paul Sier; Patrick LoPresti

doi:10.1117/12.896964

13 October 2011 High speed mask inspection data prep flow based on pipelining

Dan Hung, Domingo Morales, Juan Pablo Canepa, Stephen Kim, Po Liu, Jean-Paul Sier, Patrick LoPresti

Proceedings Volume 8166, Photomask Technology 2011; 81660R (2011) https://doi.org/10.1117/12.896964
Event: SPIE Photomask Technology, 2011, Monterey, California, United States

Abstract

Mask manufacturers are continuously challenged as a result of the explosive growth in mask pattern data volume. This paper presents a new pipelined approach to mask data preparation for inspection that significantly reduces the data preparation times compared to the conventional flows used today. The focus of this approach minimizes I/O bottlenecks and allows for higher throughput on computer clusters. This solution is optimized for the industry standard OASIS.MASK format. These enhancements in the data processing flow, along with optimizations in the data preparation system architecture, offer a more efficient and highly scalable solution for mask inspection data preparation.

Citation Download Citation

Dan Hung, Domingo Morales, Juan Pablo Canepa, Stephen Kim, Po Liu, Jean-Paul Sier, and Patrick LoPresti "High speed mask inspection data prep flow based on pipelining", Proc. SPIE 8166, Photomask Technology 2011, 81660R (13 October 2011); https://doi.org/10.1117/12.896964

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available