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Featured researches published by David Drain.


Proceedings of SPIE | 2008

Dual-layer dye-filled developer-soluble BARCs for 193-nm lithography

Jim D. Meador; Carol Beaman; Charlyn Stroud; Joyce Lowes; Zhimin Zhu; Douglas J. Guerrero; Ramil-Marcelo L. Mercado; David Drain

A family of dye-filled developer-soluble bottom anti-reflective coatings (BARCs) has been developed for use in 193-nm microlithography. This new dye-filled chemical platform easily provides products covering a wide range of optical properties. The light-sensitive and positive-working BARCs use a transparent polymeric binder and a polymeric dye in a thermally crosslinking formulation, with the cured products then being photochemically decrosslinked prior to development. The cured BARC films are imaged and removed with developer in the same steps as the covering photoresist. Two dye-filled BARCs with differing optical properties were developed via a series of DOEs and then used as a dual-layer BARC stack. Lithography with this BARC stack, using a 193-nm resist, gave 150-nm L/S (1:1). A 193-nm dual-layer BARC stack (gradient optical properties) from the well-established dye-attached family of light-sensitive BARCs also gave 150-nm L/S (1:1) with the same resist. However, the latter provided much improved line shape with no scumming. The targeted application for light-sensitive dual-layer BARCs is high-numerical aperture (NA) immersion lithography where a single-layer BARC will not afford the requisite reflection control.


Concurrent Engineering | 2008

Customer Expectation and Warranty Cost — Nominal-the-Best Case

Naresh Kumar Sharma; David Drain; Elizabeth A. Cudney; Kenneth M. Ragsdell

A fixed target, be it at zero or infinity, is assumed in Taguchis method for formulating the quality loss function (QLF). The QLF only accounts for immediate issues within manufacturing facilities whereas warranty cost occurs during customer use. Variable customer expectation has not been considered in the Taguchi methodology. This article presents a methodology to predict warranty probability, the probability of customer complaint, on the basis of two independent variables; product performance and consumer expectation. It is expected that the formulation presented will serve as a basic model for predicting warranty loss using warranty probability due to a single characteristic under certain assumptions. The nominal-the-best case is considered in this article and warranty cost is estimated for an automotive example to demonstrate the methodology.


Quality and Reliability Engineering International | 2004

A genetic algorithm hybrid for constructing optimal response surface designs

David Drain; W. Matthew Carlyle; Douglas C. Montgomery; Connie M. Borror; Christine M. Anderson-Cook


Journal of Industrial and Systems Engineering | 2009

A Comparison of the Mahalanobis-Taguchi System to A Standard Statistical Method for Defect Detection

Elizabeth A. Cudney; David Drain; K Paryani; Sharma Naresh


SAE International Journal of Materials and Manufacturing | 2008

Implications of Quality Loss Function in Unified Methodology - LTB Case with Target

Naresh Kumar Sharma; Elizabeth A. Cudney; David Drain; Kenneth M. Ragsdell


Journal of Industrial and Systems Engineering | 2008

Predicting Customer-Expectation-Based Warranty Cost for Smaller-the-Better and Larger-the-Better Performance Characteristics

Naresh Kumar Sharma; David Drain; Elizabeth A. Cudney; Kenneth M. Ragsdell; Kioumars Paryani


SAE World Congress & Exhibition | 2007

A Comparison of Techniques to Forecast Consumer Satisfaction for Vehicle Ride

Elizabeth A. Cudney; David Drain; Kenneth M. Ragsdell; Kioumars Paryani


Archive | 2007

Effective Use of Process Capability Indices for Supplier Management

Elizabeth A. Cudney; David Drain


Archive | 2005

Response Surface Design for Correlated Noise Variables

David Drain; Connie M. Borror; Christine M. Anderson-Cook


Archive | 2007

Screening for noise variables

Lisa Trautwein; David Drain; Elizabeth A. Cudney

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Elizabeth A. Cudney

Missouri University of Science and Technology

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Kenneth M. Ragsdell

Missouri University of Science and Technology

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Naresh Kumar Sharma

Missouri University of Science and Technology

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Douglas J. Guerrero

Missouri University of Science and Technology

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K Paryani

Missouri University of Science and Technology

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Sharma Naresh

Lawrence Technological University

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