2015
DOI: 10.1109/jmems.2015.2493581
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Simultaneous Control of Dielectric Charge and Device Capacitance in Electrostatic MEMS

Abstract: Abstract-This work presents a double closed-loop for controlling simultaneously the net dielectric charge and the device capacitance in contactless electrostatic MEMS devices. The first loop controls the net charge trapped in the dielectric layer by continuously monitoring the horizontal displacement of the C-V characteristic and applying bipolar actuation voltages to keep such net charge at the target value. The second loop adapts the actuation voltages so that the measured capacitance matches a desired value… Show more

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Cited by 2 publications
(3 citation statements)
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“…In switching applications, these techniques usually focus on avoiding device stiction. Instead, in the contactless scenario, control methods have been thought to offer fine tuning of dielectric charge and device capacitance [43][44][45][46].…”
Section: Charge Control Strategiesmentioning
confidence: 99%
See 1 more Smart Citation
“…In switching applications, these techniques usually focus on avoiding device stiction. Instead, in the contactless scenario, control methods have been thought to offer fine tuning of dielectric charge and device capacitance [43][44][45][46].…”
Section: Charge Control Strategiesmentioning
confidence: 99%
“…• Finally, in order to address the proposed charge control techniques to those applications in which the capacitance value is a key factor, a simultaneous control of both charge and capacitance has been provided [46].…”
Section: Main Contributions Of the Thesismentioning
confidence: 99%
“…The diffusive charge characterization method has been developed from the general diffusive theory shown in subsection 2.2.5 of this Thesis. To characterize the dynamics of the charge trapped in the dielectric layer of a MEMS system, the voltage waveforms, or symbols BIT0 and BIT1, designed for charge characterization and control in [96,79,97,110,111,98] are used. In Figure 2.22 these waveforms are shown.…”
Section: Diffusive Representation Modelingmentioning
confidence: 99%