Neural network modeling of the cellgap process for liquid crystal display fabricated on plastic substrates

Jung Hwan Lee, Dong Hun Kang, Young Don Ko, Jaejin Jang, Dae-Shik Seo, Ilgu Yun

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

In this paper, a neural network model is presented to characterize the thickness and the uniformity of the cellgap process for flexible liquid crystal display (LCD). Input factors are explored via a D-optimal design with 15 runs and used as training data in the neural network. In order to verify the fitness of the model, three more runs are added as test data. Latin hypercube sampling and error back-propagation algorithm are used to build the model. Latin hypercube sampling is used to generate initial weights and biases of the network. The thickness of cellgap is measured at five points: one at the center and four at the edges. The average thickness is used as cellgap thickness, and the uniformity is obtained by comparing the thickness at the center and edge points.

Original languageEnglish
Pages (from-to)1311-1315
Number of pages5
JournalExpert Systems with Applications
Volume35
Issue number3
DOIs
Publication statusPublished - 2008 Oct 1

Fingerprint

Liquid crystal displays
Plastics
Neural networks
Substrates
Flexible displays
Sampling
Backpropagation algorithms

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

Lee, Jung Hwan ; Kang, Dong Hun ; Ko, Young Don ; Jang, Jaejin ; Seo, Dae-Shik ; Yun, Ilgu. / Neural network modeling of the cellgap process for liquid crystal display fabricated on plastic substrates. In: Expert Systems with Applications. 2008 ; Vol. 35, No. 3. pp. 1311-1315.
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Neural network modeling of the cellgap process for liquid crystal display fabricated on plastic substrates. / Lee, Jung Hwan; Kang, Dong Hun; Ko, Young Don; Jang, Jaejin; Seo, Dae-Shik; Yun, Ilgu.

In: Expert Systems with Applications, Vol. 35, No. 3, 01.10.2008, p. 1311-1315.

Research output: Contribution to journalArticle

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