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Using neural networks as a fault detection mechanism in MEMS devices

Asgary, Reza, Mohammadi, Karim and Zwolinski, Mark, Stojadinovic, N(ed.) (2007) Using neural networks as a fault detection mechanism in MEMS devices Microelectronics Reliability, 47, (1), pp. 142-149.

Record type: Article

Abstract

Micro Electro Mechanical Systems (MEMS) were first proposed about 20 years ago. Today, many different kinds have been fabricated and are used in industry, space and scientific fields. The scale and relative size of analog integrated circuits and non-electrical parts are becoming smaller. As a result, the need for automatic test of MEMS is a critical requirement in MEMS fabrication and maintenance. The rapid progress in the design of these systems has not, however, been accompanied by a similar progress in fault classification technologies. MEMS are naturally very non-linear, complex and multi-domain and systems are fabricated near to each other. A large number of faults of different types may occur. This paper presents a combination of a Competitive Neural Network (CNN) and a Robust Heteroscedastic Probabilistic Neural Network (RHPNN) for fault detection in MEMS. The RHPNN has previously been proposed for analog fault detection. Finding the optimum kernel number in the second layer is a drawback of the RHPNN method. In this paper we have used a CNN for finding the optimum kernel number automatically. In addition, as the simulation results show, the correct fault detection percentage is increased in comparison with the RHPNN alone.

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More information

Published date: January 2007
Organisations: EEE

Identifiers

Local EPrints ID: 263411
URI: http://eprints.soton.ac.uk/id/eprint/263411
ISSN: 0026-2714
PURE UUID: b8491df2-f330-45cb-b1b5-8a4c2f4c4c05
ORCID for Mark Zwolinski: ORCID iD orcid.org/0000-0002-2230-625X

Catalogue record

Date deposited: 12 Feb 2007
Last modified: 18 Jul 2017 07:45

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Contributors

Author: Reza Asgary
Author: Karim Mohammadi
Author: Mark Zwolinski ORCID iD
Editor: N Stojadinovic

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