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Proceedings Paper

Automated process control optimization to control low volume products based on high volume products data
Author(s): Tatiana Levin; Issi Geier; Alex Zhivotovsky; Nilufar Aframiam; Hamutal Friedlander-Klar
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Paper Abstract

The quality of an Automated Process Control (APC) depends highly on the amount of relevant measurement data points. The quality of APC for low volume products is lower than high volume products, since there is not enough data to respond to tool parameters drift or incoming variations. In order to improve low volume runners control it is proposed to use high volume runners data to generate feedback for low volume runners. Product to product differences can be minimized by applying bias. This bias does not remain stable due to tool parameters drift or incoming variations. The current paper addresses these issues and reviews different methods for bias control/change if needed. Intel Litho APC is using EWMA time based weighting for parameters like Overlay parameters, Focus and Dose control. The data for each set of feedback list is segmented by several partition variables (tool, operation, etc.) within a defined expiration period. For low volume runners it is possible to widen the partition by adding main runners data with applied bias. Historical data shows possible bias variability following process or tool drifts over time. Different cases of partition biases are reviewed based on Litho parameters examples. Various algorithms for bias control and bias calculation are reviewed. Simulation studies are performed to predict the impact of deploying this strategy in production.

Paper Details

Date Published: 17 May 2005
PDF: 12 pages
Proc. SPIE 5755, Data Analysis and Modeling for Process Control II, (17 May 2005); doi: 10.1117/12.598409
Show Author Affiliations
Tatiana Levin, Intel Corp. (Israel)
Issi Geier, Intel Corp. (Israel)
Alex Zhivotovsky, Intel Corp. (Israel)
Nilufar Aframiam, Intel Corp. (Israel)
Hamutal Friedlander-Klar, Intel Corp. (Israel)

Published in SPIE Proceedings Vol. 5755:
Data Analysis and Modeling for Process Control II
Iraj Emami, Editor(s)

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