Neural network based modeling of PL intensity in PLD-grown ZnO thin films

Young Don Ko, Hong Seong Kang, Min Chang Jeong, Sang Yeol Lee, Jae Min Myoung, Ilgu Yun

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

The process modeling of ZnO thin films grown by pulsed laser deposition (PLD) was investigated using neural networks based on radial basis function networks (RBFN) and multi-layer perceptron (MLP). Two input factors were examined with respect to the response factor, photoluminescence (PL), which is one of the main factors to determine the optical characteristic of the structure. In order to minimize the joint confidence region of fabrication process with varying the conditions, D-optimal experimental design technique was performed and PL intensity was characterized by neural networks. The statistical results were then used to verify the fitness of the nonlinear process model. Based on the results, this modeling methodology can optimize the process conditions for semiconductor manufacturing.

Original languageEnglish
Pages (from-to)159-163
Number of pages5
JournalJournal of Materials Processing Technology
Volume159
Issue number2
DOIs
Publication statusPublished - 2005 Jan 30

Bibliographical note

Funding Information:
This work was supported by the Brain Korea 21 project in 2003.

All Science Journal Classification (ASJC) codes

  • Ceramics and Composites
  • Computer Science Applications
  • Metals and Alloys
  • Industrial and Manufacturing Engineering

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