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Proceedings Paper

Comparison of OPC job prioritization schemes to generate data for mask manufacturing
Author(s): Travis Lewis; Vijay Veeraraghavan; Kenneth Jantzen; Stephen Kim; Minyoung Park; Gordon Russell; Mark Simmons
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Paper Abstract

Delivering mask ready OPC corrected data to the mask shop on-time is critical for a foundry to meet the cycle time commitment for a new product. With current OPC compute resource sharing technology, different job scheduling algorithms are possible, such as, priority based resource allocation and fair share resource allocation. In order to maximize computer cluster efficiency, minimize the cost of the data processing and deliver data on schedule, the trade-offs of each scheduling algorithm need to be understood. Using actual production jobs, each of the scheduling algorithms will be tested in a production tape-out environment. Each scheduling algorithm will be judged on its ability to deliver data on schedule and the trade-offs associated with each method will be analyzed. It is now possible to introduce advance scheduling algorithms to the OPC data processing environment to meet the goals of on-time delivery of mask ready OPC data while maximizing efficiency and reducing cost.

Paper Details

Date Published: 18 March 2015
PDF: 9 pages
Proc. SPIE 9427, Design-Process-Technology Co-optimization for Manufacturability IX, 942711 (18 March 2015); doi: 10.1117/12.2086927
Show Author Affiliations
Travis Lewis, GLOBALFOUNDRIES Inc. (United States)
Vijay Veeraraghavan, GLOBALFOUNDRIES Inc. (United States)
Kenneth Jantzen, Mentor Graphics Corp. (United States)
Stephen Kim, Mentor Graphics Corp. (United States)
Minyoung Park, Mentor Graphics Corp. (United States)
Gordon Russell, Mentor Graphics Corp. (United States)
Mark Simmons, Mentor Graphics Corp. (United States)

Published in SPIE Proceedings Vol. 9427:
Design-Process-Technology Co-optimization for Manufacturability IX
John L. Sturtevant; Luigi Capodieci, Editor(s)

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