Behavioural Modelling, Simulation, Test and Diagnosis of MEMS using ANNs
Behavioural Modelling, Simulation, Test and Diagnosis of MEMS using ANNs
The design of Micro-Electrical-Mechanical Systems requires that the entire system can be modelled and simulated. Additionally, behaviour under fault conditions must be simulated to determine test and diagnosis strategies. While the electrical parts of a system can be modelled at transistor, gate or behavioural levels, the mechanical parts are conventionally modelled in terms of partial differential equations (PDEs). Mixed-signal electrical simulations are possible, using e.g. VHDL-AMS, but simulations that include PDEs are prohibitively expensive. Here, we show that complex PDEs can be replaced by black-box functional models and, importantly, such models can be characterized automatically and rapidly using artificial neural networks (ANNs). We demonstrate a significant increase in simulation speed and show that test and diagnosis strategies can be derived using such models.
Litovski, V
6d93668f-3784-453f-bd36-f9d3d7bdd9a5
Andrejević, M
026017d3-2394-4e30-821c-8310ac9ee6e9
Zwolinski, M
adfcb8e7-877f-4bd7-9b55-7553b6cb3ea0
2005
Litovski, V
6d93668f-3784-453f-bd36-f9d3d7bdd9a5
Andrejević, M
026017d3-2394-4e30-821c-8310ac9ee6e9
Zwolinski, M
adfcb8e7-877f-4bd7-9b55-7553b6cb3ea0
Litovski, V, Andrejević, M and Zwolinski, M
(2005)
Behavioural Modelling, Simulation, Test and Diagnosis of MEMS using ANNs.
International Symposium on Circuits and Systems, Kobe, Japan.
23 - 26 May 2005.
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Conference or Workshop Item
(Paper)
Abstract
The design of Micro-Electrical-Mechanical Systems requires that the entire system can be modelled and simulated. Additionally, behaviour under fault conditions must be simulated to determine test and diagnosis strategies. While the electrical parts of a system can be modelled at transistor, gate or behavioural levels, the mechanical parts are conventionally modelled in terms of partial differential equations (PDEs). Mixed-signal electrical simulations are possible, using e.g. VHDL-AMS, but simulations that include PDEs are prohibitively expensive. Here, we show that complex PDEs can be replaced by black-box functional models and, importantly, such models can be characterized automatically and rapidly using artificial neural networks (ANNs). We demonstrate a significant increase in simulation speed and show that test and diagnosis strategies can be derived using such models.
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Published date: 2005
Additional Information:
Event Dates: 23-26 May 2005
Venue - Dates:
International Symposium on Circuits and Systems, Kobe, Japan, 2005-05-23 - 2005-05-26
Organisations:
EEE
Identifiers
Local EPrints ID: 260432
URI: http://eprints.soton.ac.uk/id/eprint/260432
PURE UUID: bbe75f50-0710-461c-befc-3fdc7902882f
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Date deposited: 03 Feb 2005
Last modified: 15 Mar 2024 02:39
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Contributors
Author:
V Litovski
Author:
M Andrejević
Author:
M Zwolinski
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