Taguchi method-ANN integration for predictive model of intrinsic stress in hydrogenated amorphous silicon film deposited by plasma enhanced chemical vapour deposition

T. B. Asafa, Nouar Tabet, S. A M Said

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

An integration of Taguchi method and artificial neural network (ANN) technique for the prediction of intrinsic stresses induced during plasma enhanced chemical vapor deposition (PECVD) of hydrogenated amorphous silicon (a-Si:H) thin films is presented. Inputs to the ANN model are plasma power, hydrogen dilution ratio, chamber pressure and substrate temperature. Ninety-two data points were used for the network training, model validation and testing in a 2:1:1 relative proportion. An optimized model with a network architecture of 4-5-3-1, a Levenberg-Marquardt training algorithm and a learning rate of 0.1 was obtained from L9 (34) orthogonal array based on Taguchi approach. By using the optimized network, parametric studies were conducted to show how the intrinsic stresses are influenced by the deposition parameters. Analysis of variance (ANOVA) of the ANN variables indicates that the first hidden layer is the most significant parameter contributing about 39% to the changes in the network mean square error (MSE) while the second hidden layer contributes about 15%. Accuracies of the predictive model are within ±2.5% and ±13% error bound for compressive and tensile stress regimes, respectively. Also, results of the parametric study show a clear trend between the deposition parameters and the resulting intrinsic stresses, and are found to agree with published data. The results are discussed in the light of physics of PECVD process.

Original languageEnglish
Pages (from-to)86-94
Number of pages9
JournalNeurocomputing
Volume106
DOIs
Publication statusPublished - 1 Jan 2013
Externally publishedYes

Fingerprint

Taguchi methods
Silicon
Plasma enhanced chemical vapor deposition
Amorphous silicon
Neural networks
Neural Networks (Computer)
Physics
Hydrogen
Analysis of Variance
Analysis of variance (ANOVA)
Network architecture
Compressive stress
Learning
Tensile stress
Mean square error
Dilution
Pressure
Temperature
Plasmas
Thin films

Keywords

  • ANN
  • Hydrogenated amorphous silicon
  • Intrinsic stress
  • PECVD
  • Taguchi method

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Cognitive Neuroscience

Cite this

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title = "Taguchi method-ANN integration for predictive model of intrinsic stress in hydrogenated amorphous silicon film deposited by plasma enhanced chemical vapour deposition",
abstract = "An integration of Taguchi method and artificial neural network (ANN) technique for the prediction of intrinsic stresses induced during plasma enhanced chemical vapor deposition (PECVD) of hydrogenated amorphous silicon (a-Si:H) thin films is presented. Inputs to the ANN model are plasma power, hydrogen dilution ratio, chamber pressure and substrate temperature. Ninety-two data points were used for the network training, model validation and testing in a 2:1:1 relative proportion. An optimized model with a network architecture of 4-5-3-1, a Levenberg-Marquardt training algorithm and a learning rate of 0.1 was obtained from L9 (34) orthogonal array based on Taguchi approach. By using the optimized network, parametric studies were conducted to show how the intrinsic stresses are influenced by the deposition parameters. Analysis of variance (ANOVA) of the ANN variables indicates that the first hidden layer is the most significant parameter contributing about 39{\%} to the changes in the network mean square error (MSE) while the second hidden layer contributes about 15{\%}. Accuracies of the predictive model are within ±2.5{\%} and ±13{\%} error bound for compressive and tensile stress regimes, respectively. Also, results of the parametric study show a clear trend between the deposition parameters and the resulting intrinsic stresses, and are found to agree with published data. The results are discussed in the light of physics of PECVD process.",
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N2 - An integration of Taguchi method and artificial neural network (ANN) technique for the prediction of intrinsic stresses induced during plasma enhanced chemical vapor deposition (PECVD) of hydrogenated amorphous silicon (a-Si:H) thin films is presented. Inputs to the ANN model are plasma power, hydrogen dilution ratio, chamber pressure and substrate temperature. Ninety-two data points were used for the network training, model validation and testing in a 2:1:1 relative proportion. An optimized model with a network architecture of 4-5-3-1, a Levenberg-Marquardt training algorithm and a learning rate of 0.1 was obtained from L9 (34) orthogonal array based on Taguchi approach. By using the optimized network, parametric studies were conducted to show how the intrinsic stresses are influenced by the deposition parameters. Analysis of variance (ANOVA) of the ANN variables indicates that the first hidden layer is the most significant parameter contributing about 39% to the changes in the network mean square error (MSE) while the second hidden layer contributes about 15%. Accuracies of the predictive model are within ±2.5% and ±13% error bound for compressive and tensile stress regimes, respectively. Also, results of the parametric study show a clear trend between the deposition parameters and the resulting intrinsic stresses, and are found to agree with published data. The results are discussed in the light of physics of PECVD process.

AB - An integration of Taguchi method and artificial neural network (ANN) technique for the prediction of intrinsic stresses induced during plasma enhanced chemical vapor deposition (PECVD) of hydrogenated amorphous silicon (a-Si:H) thin films is presented. Inputs to the ANN model are plasma power, hydrogen dilution ratio, chamber pressure and substrate temperature. Ninety-two data points were used for the network training, model validation and testing in a 2:1:1 relative proportion. An optimized model with a network architecture of 4-5-3-1, a Levenberg-Marquardt training algorithm and a learning rate of 0.1 was obtained from L9 (34) orthogonal array based on Taguchi approach. By using the optimized network, parametric studies were conducted to show how the intrinsic stresses are influenced by the deposition parameters. Analysis of variance (ANOVA) of the ANN variables indicates that the first hidden layer is the most significant parameter contributing about 39% to the changes in the network mean square error (MSE) while the second hidden layer contributes about 15%. Accuracies of the predictive model are within ±2.5% and ±13% error bound for compressive and tensile stress regimes, respectively. Also, results of the parametric study show a clear trend between the deposition parameters and the resulting intrinsic stresses, and are found to agree with published data. The results are discussed in the light of physics of PECVD process.

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