This paper presents our work on the design, microfabrication and testing of a bidirectional microactuator. The microactuator is electromagnetically driven and it is fully fabricated by surface micromachining on a single silicon wafer with CMOS compatibility. The microactuator consists of three main parts: a Au microcoil, a NiFe membrane with supported legs and a Co–Pt permanent magnet integrated on the membrane. The microactuator has an overall diameter of 1600 µm and an overall height of 600 µm, including the wafer thickness. Two different membrane designs are presented: double-leg and single-leg designs. A laser Doppler vibrometer is used to test the bidirectional motion of the microactuator. The double-leg design operates at a current in the range of −0.25 to 0.3 A for a displacement in the range of −71 to 28.3 µm. The single-leg design operates at a current in the range of −0.1 to 0.3 A for a displacement in the range of −108 to 150 µm. The bidirectional microactuator has shown a large displacement/size ratio, up to 35 µm mm−2.
We present the use of artificial neural networks ͑ANNs͒ to model an electromagnetic microelectromechanical system ͑MEMS͒ 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 methanol͒. ANNs are used to model the maximum displacement ͑d max ͒ of the microactuator for a range of burst frequencies ͑f b ͒ and input currents ͑I coil ͒, as well as different mechanical designs and actuation media. The prediction errors of the ANN model in normal and pressurized air are Ͻ13 and Ͻ2%, respectively. The prediction error for the same response in water or 50% diluted methanol in water is Ͻ10%.
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