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Journal of Micro/Nanolithography, MEMS, and MOEMS

Using neural networks to model an electromagnetic-actuated microactuator
Author(s): Jemmy Sutanto; Ronald Setia; Adam Papania; Gary Stephen May; Peter J. Hesketh; Yves H. Berthelot
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Paper Abstract

We present the use of artificial neural networks (ANNs) to model an electromagnetic microelectromechanical system (MEMS) microactuator. It is inherently complex and time consuming to model/predict the response of an electromagnetic microactuator numerically by finite element analysis, particularly when it is actuated by a pulse of current in media with different properties (e.g., air, water, and diluted methanol). ANNs are used to model the maximum displacement (dmax) of the microactuator for a range of burst frequencies (fb) and input currents (Icoil), as well as different mechanical designs and actuation media. The prediction errors of the ANN model in normal and pressurized air are <13 and <2%, respectively. The prediction error for the same response in water or 50% diluted methanol in water is <10%.

Paper Details

Date Published: 1 January 2007
PDF: 10 pages
J. Micro/Nanolith. 6(1) 013011 doi: 10.1117/1.2712864
Published in: Journal of Micro/Nanolithography, MEMS, and MOEMS Volume 6, Issue 1
Show Author Affiliations
Jemmy Sutanto, Intel Corp. (United States)
Ronald Setia, Intel Corp. (United States)
Adam Papania, Georgia Institute of Technology (United States)
Gary Stephen May, Georgia Institute of Technology (United States)
Peter J. Hesketh, Georgia Institute of Technology (United States)
Yves H. Berthelot, Georgia Institute of Technology (United States)


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