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The preparation of organic light-emitting diode encapsulation barrier layer by low-temperature plasma-enhanced chemical vapor deposition: a study on the $$\hbox {SiO}_\mathrm{x}\hbox {N}_\mathrm{y}$$ SiO x N y film parameter optimization

Author

Listed:
  • Chung-Feng Jeffrey Kuo

    (National Taiwan University of Science and Technology)

  • Wei-Lun Lan

    (National Taiwan University of Science and Technology)

  • Yu-Cheng Chang

    (National Taiwan University of Science and Technology)

  • Kun-Wei Lin

    (Industrial Technology Research Institute)

Abstract

This study prepared $$\hbox {SiO}_\mathrm{x}\hbox {N}_\mathrm{y}$$ SiO x N y film by using plasma-enhanced chemical vapor deposition in organic light-emitting diode (OLED) encapsulation to prevent the invasion of moisture and oxygen for longer light-emitting lifetime of OLED components. It applied high density inductively coupled plasma for the coating of film on polyethersulfone, silicon and glass substrate, and discussed the relevance between process parameters and quality characteristics including coating uniformity, coating thickness and moisture permeation. This study used Taguchi method to plan the experiment and calculated the optimal parameters of each quality, used technique for order preference by similarity to ideal solution and grey relational analysis to determine the optimal parameter of all qualities. The back-propagation neural network was combined with Levenberg–Marquardt algorithm to construct the simulation and prediction system. Based on the quality optimization design, the single layer film’s moisture permeation rate was 0.02 g/m $$^{2}$$ 2 /day, the maximum coating thickness reached 420 nm, and the fastest rate was 21 nm/min, which was higher than the industrial standard specification (10 nm/min) by 110 %.

Suggested Citation

  • Chung-Feng Jeffrey Kuo & Wei-Lun Lan & Yu-Cheng Chang & Kun-Wei Lin, 2016. "The preparation of organic light-emitting diode encapsulation barrier layer by low-temperature plasma-enhanced chemical vapor deposition: a study on the $$\hbox {SiO}_\mathrm{x}\hbox {N}_\mathrm{y}$$ ," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 581-593, June.
  • Handle: RePEc:spr:joinma:v:27:y:2016:i:3:d:10.1007_s10845-014-0893-8
    DOI: 10.1007/s10845-014-0893-8
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    Cited by:

    1. P. Luangpaiboon & S. Boonhao & R. Montemanni, 2019. "Steepest ant sense algorithm for parameter optimisation of multi-response processes based on taguchi design," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 441-457, January.

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