2012Applied OpticsRequires access

Advanced error identification in deposition of complex optical layer systems by a multianalyzing approach

Carsten Schmitz, Henrik Ehlers, Detlev Ristau

Open publisher page 2 citations

Abstract

If a new complex optical multilayer system, coating chamber, material, or design has to be evaluated, there is often a need for several test deposition runs until most significant errors and coating properties are identified. We present an advanced procedure with combination of an optical broadband thickness monitor, computational manufacturing, and automated reoptimization, which requires only one single test deposition run. For the identification of material and deposition errors, the single test deposition run is evaluated by the computational manufacturing using different parameter sets. Determined main errors are corrected (e.g., dispersion), and remaining smaller errors will be compensated with the automated reoptimization tool as an expansion of the optical monitor.

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What this paper is about

If a new complex optical multilayer system, coating chamber, material, or design has to be evaluated, there is often a need for several test deposition runs until most significant errors and coating properties are identified. We present an advanced procedure with combination of an optical broadband thickness monitor, computational manufacturing, and automated reoptimization, which requires only one single test deposition run. For the identification of material and deposition errors, the single test deposition run is evaluated by the computational manufacturing using different parameter sets. Determined main errors are corrected (e.g., dispersion), and remaining smaller errors will be compensated with the automated reoptimization tool as an expansion of the optical monitor.

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Available abstract

If a new complex optical multilayer system, coating chamber, material, or design has to be evaluated, there is often a need for several test deposition runs until most significant errors and coating properties are identified. We present an advanced procedure with combination of an optical broadband thickness monitor, computational manufacturing, and automated reoptimization, which requires only one single test deposition run. For the identification of material and deposition errors, the single test deposition run is evaluated by the computational manufacturing using different parameter sets. Determined main errors are corrected (e.g., dispersion), and remaining smaller errors will be compensated with the automated reoptimization tool as an expansion of the optical monitor.

Key concepts: Deposition (geology), Coating, Optical coating, Identification (biology), Layer (electronics), Computer science, Optics, Broadband

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