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• W2132227131 abstract "Abstract The analysis of data from simulations and experiments in the developmentphase and measurements during mass production plays a crucial role in mod-ern manufacturing: Experiments and simulations are performed during thedevelopment phase to ensure the design’s fitness for mass production. Duringproduction, a large number of measurements in the automated production linecontrols a stable quality.As the number of measurements grows, the conventional, largely manualdata analysis approaches its limits, and alternative methods are needed. Thisthesis studies the value of machine learning methods for typical problems facedin data analysis from engineering to mass production. In a case-study, the pro-duction of integrated circuits and micro electro-mechanical systems in silicontechnology is discussed in detail. A number of approaches to salient problemsin industrial application have been developed in the presented work, addressingthe yield as the central figure of batch processes in silicon manufacturing:The parametric yield is governed by a design’s robustness against processtolerances. This work develops a framework for doing statistical sensitivityanalysis, and robust optimization which accounts for process tolerances. Us-ing nonparametric Gaussian process regression, the sensitivity analysis can beperformed efficiently. For computationally demanding simulations a robustoptimization is eventually only made feasible through the presented approach.Being probabilistic models, Gaussian processes allow for an optimal exper-imental design, thus significantly reducing the number of required simulationruns. A novel approach to active learning for Gaussian process regression isproposed in this thesis, and validated experimentally.Besides random failures, as captured by the parametric yield, systematicerrors in the production can lead to additional losses. It is hard to localize theroot cause for previously unseen losses, as physical interrelations can hardlybe reconstructed in complex manufacturing facilities, and as there is usually alarge number of potential sources for the error. This work shows that, usingfeature selection, data from quality checks can be combined with data frommanufacturing to construct an automated localization mechanism." @default.
• W2132227131 created "2016-06-24" @default.
• W2132227131 creator A5038659950 @default.
• W2132227131 date "2007-01-01" @default.
• W2132227131 modified "2023-09-27" @default.
• W2132227131 title "Machine Learning for Mass Production and Industrial Engineering" @default.
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