Fault Diagnosis On Vlsi Adder Circuits Using Artificial Neural Network

Fault diagnosis on VLSI digital circuit is a technique to detect a fault and the location of the fault that present in a VLSI digital circuit. A faulty circuit in an IC can cause the IC to be malfunctioning and unusable. Therefore, the faulty circuit must be detected during the manufacturing process...

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Main Author:	Pui , Min San
Format:	Thesis
Language:	English
Published:	2015
Subjects:	TK7800-8360 Electronics
Online Access:	http://eprints.usm.my/40985/1/PUI_MIN_SAN_24_pages.pdf
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id	my-usm-ep.40985
record_format	uketd_dc
spelling	my-usm-ep.409852018-07-11T07:18:00Z Fault Diagnosis On Vlsi Adder Circuits Using Artificial Neural Network 2015 Pui , Min San TK7800-8360 Electronics Fault diagnosis on VLSI digital circuit is a technique to detect a fault and the location of the fault that present in a VLSI digital circuit. A faulty circuit in an IC can cause the IC to be malfunctioning and unusable. Therefore, the faulty circuit must be detected during the manufacturing process to prevent the faulty IC from delivering to the customers, which can cause a reliability issue. Manual fault diagnosis, which is currently being used in industries, is carried out by the analysis of the human. However, the manual fault diagnosis, which relies heavily on the massive analysis by the human, has several disadvantages such as long diagnosis time for complicated fault diagnosis case, manual analysis, accuracy dependent to human errors, complexity of the faulty circuits, human intelligence, etc. Therefore, in this study, a new fault diagnosis approach, which is based on artificial neural network, is proposed to detect a fault and the fault location with better accuracy and diagnosis time if compared to the manual fault diagnosis. In this study, the proposed fault diagnosis is designed based on the hierarchical neural network, which transforms each of the full-adder circuits to an individual artificial neural network. The proposed fault diagnosis has 100% accuracy when it is applied to the 4-bit adder, 5-bit adder and 6-bit adder as well as one second diagnosis time. On the other hand, the manual fault diagnosis may take up to a few days to diagnose the adder circuits. Thus, the proposed automated fault diagnosis has high potential to be applied for VLSI digital circuits in industries. 2015 Thesis http://eprints.usm.my/40985/ http://eprints.usm.my/40985/1/PUI_MIN_SAN_24_pages.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik dan Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TK7800-8360 Electronics
spellingShingle	TK7800-8360 Electronics Pui , Min San Fault Diagnosis On Vlsi Adder Circuits Using Artificial Neural Network
description	Fault diagnosis on VLSI digital circuit is a technique to detect a fault and the location of the fault that present in a VLSI digital circuit. A faulty circuit in an IC can cause the IC to be malfunctioning and unusable. Therefore, the faulty circuit must be detected during the manufacturing process to prevent the faulty IC from delivering to the customers, which can cause a reliability issue. Manual fault diagnosis, which is currently being used in industries, is carried out by the analysis of the human. However, the manual fault diagnosis, which relies heavily on the massive analysis by the human, has several disadvantages such as long diagnosis time for complicated fault diagnosis case, manual analysis, accuracy dependent to human errors, complexity of the faulty circuits, human intelligence, etc. Therefore, in this study, a new fault diagnosis approach, which is based on artificial neural network, is proposed to detect a fault and the fault location with better accuracy and diagnosis time if compared to the manual fault diagnosis. In this study, the proposed fault diagnosis is designed based on the hierarchical neural network, which transforms each of the full-adder circuits to an individual artificial neural network. The proposed fault diagnosis has 100% accuracy when it is applied to the 4-bit adder, 5-bit adder and 6-bit adder as well as one second diagnosis time. On the other hand, the manual fault diagnosis may take up to a few days to diagnose the adder circuits. Thus, the proposed automated fault diagnosis has high potential to be applied for VLSI digital circuits in industries.
format	Thesis
qualification_level	Master's degree
author	Pui , Min San
author_facet	Pui , Min San
author_sort	Pui , Min San
title	Fault Diagnosis On Vlsi Adder Circuits Using Artificial Neural Network
title_short	Fault Diagnosis On Vlsi Adder Circuits Using Artificial Neural Network
title_full	Fault Diagnosis On Vlsi Adder Circuits Using Artificial Neural Network
title_fullStr	Fault Diagnosis On Vlsi Adder Circuits Using Artificial Neural Network
title_full_unstemmed	Fault Diagnosis On Vlsi Adder Circuits Using Artificial Neural Network
title_sort	fault diagnosis on vlsi adder circuits using artificial neural network
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Elektrik dan Elektronik
publishDate	2015
url	http://eprints.usm.my/40985/1/PUI_MIN_SAN_24_pages.pdf
_version_	1747820853701115904

Fault Diagnosis On Vlsi Adder Circuits Using Artificial Neural Network

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