An artificial neural network approach for classification and prediction of low pressure chemical vapor deposition SixNy ISFET pH sensor drift

A sensor in an ion selective field-effect transistor (ISFET) is a field sensor in which ions within a sample media undergo multiple environments affecting reactions occurring molecules to concentrate at the gate oxide layer. A change in electrical charge occurs and it affects the conductance in the...

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Main Author:	Mohammad Iqwhanus Syaffa, Amir
Format:	Thesis
Language:	English
Published:	2019
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/30027/1/An%20artificial%20neural%20network%20approach%20for%20classification%20and%20prediction%20of%20low%20pressure%20chemical%20vapor.wm.pdf
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spelling	my-ump-ir.300272023-02-08T04:32:50Z An artificial neural network approach for classification and prediction of low pressure chemical vapor deposition SixNy ISFET pH sensor drift 2019-08 Mohammad Iqwhanus Syaffa, Amir TK Electrical engineering. Electronics Nuclear engineering A sensor in an ion selective field-effect transistor (ISFET) is a field sensor in which ions within a sample media undergo multiple environments affecting reactions occurring molecules to concentrate at the gate oxide layer. A change in electrical charge occurs and it affects the conductance in the ISFET channels. Consequently, the change in conductance within the source and the drain produce an electrical signal. The most common problem is the occurrence of drift when the electrical signal output gradually changes independent of the measured sample. The primary objective of the present study is to investigate a reliable model based on artificial neural network to classify and forecast errors as well as to implement the drift compensation in low-pressure chemical vapour deposition SixNy ISFET pH sensor. Such model could be used to encounter voltage drift problems that usually exist in chemical sensors. Three units of ISFET sensors were used to calibrate with three types of pH buffer solutions that are pH 4, pH 7 and pH 10. Artificial neural networks were applied to construct a black-box with multiple inputs and outputs models of the ISFET data. A percentage accuracy value was used to assess the model’s performances in terms of classification, while the mean squared error (MSE) and the coefficient of determination (R2) parameter were used to determine the best models in predicting errors in the ISFET sensors. On the model’s structure in classification, Pattern Recognition Neural Network (PATTERNNET) proved to perform better than Function Fitting (FITNET) networks with 100% accuracy. The network configuration in PATTERNNET, a dual-layered network with 30 nodes on the first hidden layer and 3 nodes on the second hidden layer achieved the best results. As for prediction, the NARXBR model with 75 delays produced an efficient model in predicting errors in ISFET data set. The value of MSE = 4.8814e-5 and R2 = 0.99930 for the NARX-BR model showed that the model is capable of predicting errors. The drift compensation was applied and the drift issues in the ISFET sensors were successfully resolved. The present study demonstrates significant potentials in the development of artificial neural networks to stave off voltage drift issues in ISFET low-pressure chemical vapour deposition SixNy ISFET pH sensor. 2019-08 Thesis http://umpir.ump.edu.my/id/eprint/30027/ http://umpir.ump.edu.my/id/eprint/30027/1/An%20artificial%20neural%20network%20approach%20for%20classification%20and%20prediction%20of%20low%20pressure%20chemical%20vapor.wm.pdf pdf en public masters Universiti Malaysia Pahang Faculty of Electrical & Electronics Engineering Othman, Md. Rizal
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
advisor	Othman, Md. Rizal
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Mohammad Iqwhanus Syaffa, Amir An artificial neural network approach for classification and prediction of low pressure chemical vapor deposition SixNy ISFET pH sensor drift
description	A sensor in an ion selective field-effect transistor (ISFET) is a field sensor in which ions within a sample media undergo multiple environments affecting reactions occurring molecules to concentrate at the gate oxide layer. A change in electrical charge occurs and it affects the conductance in the ISFET channels. Consequently, the change in conductance within the source and the drain produce an electrical signal. The most common problem is the occurrence of drift when the electrical signal output gradually changes independent of the measured sample. The primary objective of the present study is to investigate a reliable model based on artificial neural network to classify and forecast errors as well as to implement the drift compensation in low-pressure chemical vapour deposition SixNy ISFET pH sensor. Such model could be used to encounter voltage drift problems that usually exist in chemical sensors. Three units of ISFET sensors were used to calibrate with three types of pH buffer solutions that are pH 4, pH 7 and pH 10. Artificial neural networks were applied to construct a black-box with multiple inputs and outputs models of the ISFET data. A percentage accuracy value was used to assess the model’s performances in terms of classification, while the mean squared error (MSE) and the coefficient of determination (R2) parameter were used to determine the best models in predicting errors in the ISFET sensors. On the model’s structure in classification, Pattern Recognition Neural Network (PATTERNNET) proved to perform better than Function Fitting (FITNET) networks with 100% accuracy. The network configuration in PATTERNNET, a dual-layered network with 30 nodes on the first hidden layer and 3 nodes on the second hidden layer achieved the best results. As for prediction, the NARXBR model with 75 delays produced an efficient model in predicting errors in ISFET data set. The value of MSE = 4.8814e-5 and R2 = 0.99930 for the NARX-BR model showed that the model is capable of predicting errors. The drift compensation was applied and the drift issues in the ISFET sensors were successfully resolved. The present study demonstrates significant potentials in the development of artificial neural networks to stave off voltage drift issues in ISFET low-pressure chemical vapour deposition SixNy ISFET pH sensor.
format	Thesis
qualification_level	Master's degree
author	Mohammad Iqwhanus Syaffa, Amir
author_facet	Mohammad Iqwhanus Syaffa, Amir
author_sort	Mohammad Iqwhanus Syaffa, Amir
title	An artificial neural network approach for classification and prediction of low pressure chemical vapor deposition SixNy ISFET pH sensor drift
title_short	An artificial neural network approach for classification and prediction of low pressure chemical vapor deposition SixNy ISFET pH sensor drift
title_full	An artificial neural network approach for classification and prediction of low pressure chemical vapor deposition SixNy ISFET pH sensor drift
title_fullStr	An artificial neural network approach for classification and prediction of low pressure chemical vapor deposition SixNy ISFET pH sensor drift
title_full_unstemmed	An artificial neural network approach for classification and prediction of low pressure chemical vapor deposition SixNy ISFET pH sensor drift
title_sort	artificial neural network approach for classification and prediction of low pressure chemical vapor deposition sixny isfet ph sensor drift
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Electrical & Electronics Engineering
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/30027/1/An%20artificial%20neural%20network%20approach%20for%20classification%20and%20prediction%20of%20low%20pressure%20chemical%20vapor.wm.pdf
_version_	1783732131456876544

An artificial neural network approach for classification and prediction of low pressure chemical vapor deposition SixNy ISFET pH sensor drift

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