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Proceedings Paper

Predicting mask yields through the use of a yield model
Author(s): Andrew D. Pond
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Paper Abstract

Yield models have been successfully employed in wafer fabricators to provide data on yield learning, design for manufacturability, and the estimation of production cost for semiconductors. This paper describes how a yield model can be developed to provide important technical information for mask-making in the semiconductor industry. This information, focusing on manufacturing line loading strategy and customer charges, is different from that provided by wafer fabricators' yield models, however the underlying goal is the same: to estimate accurately the expected yield for a part produced on the manufacturing line. If this estimation is not done accurately, there can be serious cost and serviceability implications. The premise here is to categorize parts based on their expected yield (derived from the yield model) which is, itself, a function of how difficult they are to build. This model was developed using logistic regression analysis on historical data. Logistic regression has been used most commonly and successfully in epidemiological research where, for instance, the risk of an individual developing a certain type of cancer is modeled as a function of personal characteristics. methodological details of yield model development and performance monitoring are presented as well as a specific example.

Paper Details

Date Published: 7 December 1994
PDF: 10 pages
Proc. SPIE 2322, 14th Annual BACUS Symposium on Photomask Technology and Management, (7 December 1994); doi: 10.1117/12.195828
Show Author Affiliations
Andrew D. Pond, IBM Microelecronics (United States)


Published in SPIE Proceedings Vol. 2322:
14th Annual BACUS Symposium on Photomask Technology and Management
William L. Brodsky; Gilbert V. Shelden, Editor(s)

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