Please use this identifier to cite or link to this item: https://doi.org/10.1023/B:ALOG.0000032595.72995.62
Title: Neural network approach for linearization of the electrostatically actuated double-gimballed micromirror
Authors: Zhou, G. 
Cheo, K.K.L. 
Tay, F.E.H. 
Chau, F.S. 
Keywords: MEMS
Micromirror and macro modeling
Optical MEMS
Issue Date: Aug-2004
Citation: Zhou, G., Cheo, K.K.L., Tay, F.E.H., Chau, F.S. (2004-08). Neural network approach for linearization of the electrostatically actuated double-gimballed micromirror. Analog Integrated Circuits and Signal Processing 40 (2) : 141-153. ScholarBank@NUS Repository. https://doi.org/10.1023/B:ALOG.0000032595.72995.62
Abstract: In this paper, a hierarchical circuit based approach is used for the development of a reduced-order macro-model for a double-gimballed electrostatic torsional micromirror. The nonlinearity and cross-axis coupling of the micromirror subjected to the differential driving scheme are investigated using the proposed macro-model. The simulation results are used to train a feed-forward neural network which carries out a function approximation of the relation between the desired location and the required driving voltages. The trained neural network is then coded into MAST AHDL. System-level simulation of the micromirror together with the neural network is performed in the SABER™ simulator. It is found that using a feed-forward neural network, the linearity of the micromirror is greatly improved, the steady state of the cross-axis coupling is reduced to a negligible level and the transient response of the cross-axis coupling is also suppressed. This implies that introducing a feed-forward neural network would be useful to simplify the design of the feedback control system for the double-gimballed electrostatic torsional micromirror.
Source Title: Analog Integrated Circuits and Signal Processing
URI: http://scholarbank.nus.edu.sg/handle/10635/73677
ISSN: 09251030
DOI: 10.1023/B:ALOG.0000032595.72995.62
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