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Computer Science > Neural and Evolutionary Computing

arXiv:2410.21294 (cs)
[Submitted on 14 Oct 2024]

Title:Optimization of Complex Process, Based on Design Of Experiments, a Generic Methodology

Authors:Julien Baderot, Yann Cauchepin (UCA), Alexandre Seiller (UCA), Richard Fontanges, Sergio Martinez, Johann Foucher, Emmanuel Fuchs, Mehdi Daanoune, Vincent Grenier, Vincent Barra (UCA), Arnaud Guillin (UCA)
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Abstract:MicroLED displays are the result of a complex manufacturing chain. Each stage of this process, if optimized, contributes to achieving the highest levels of final efficiencies. Common works carried out by Pollen Metrology, Aledia, and Universit{é} Clermont-Auvergne led to a generic process optimization workflow. This software solution offers a holistic approach where stages are chained together for gaining a complete optimal solution. This paper highlights key corners of the methodology, validated by the experiments and process experts: data cleaning and multi-objective optimization.
Comments: Eurodisplay 2024, Sep 2024, Grenoble, France
Subjects: Neural and Evolutionary Computing (cs.NE); Optimization and Control (math.OC)
Cite as: arXiv:2410.21294 [cs.NE]
  (or arXiv:2410.21294v1 [cs.NE] for this version)
  https://doi.org/10.48550/arXiv.2410.21294

Submission history

From: Yann Cauchepin [view email] [via CCSD proxy]
[v1] Mon, 14 Oct 2024 08:05:56 UTC (392 KB)
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