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Characterization and modeling of pattern dependencies and time evolution in plasma etching

Author(s)
Farahanchi, Ali
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Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Advisor
Duane S. Boning.
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M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
A quantitative model capturing pattern dependent effects and time evolution of the etch rate in Deep Reactive Ion Etching (DRIE) is presented. DRIE is a key process for pattern formation in semiconductor fabrication. Non-uniformities are caused due to microloading and aspect ratio dependencies. The etch rate varies over time and lateral etch consumes some of the etching species. This thesis contributes a physical analysis for capturing and modeling microloading, aspect ratio dependencies, effects of lateral etch and time evolution of the etch rate. This methodology is applied to the study of etching variation on silicon wafers; the integrated model is able to predict pattern density and feature size dependent non-uniformities in trench depth and time evolution of the etch rate. Previous studies of variation in plasma etching have characterized microloading and aspect ratio dependent etching (ARDE) as distinct constant causes for etch non-uniformity. In contrast to these previous works, we present here a time-based methodology for vertical and lateral etch.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, June 2009.
 
"May 2008." Cataloged from PDF version of thesis.
 
Includes bibliographical references (p. 43).
 
Date issued
2009
URI
http://hdl.handle.net/1721.1/55265
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

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