In this paper, we propose a new method of global optimal design with simulated annealing (SA) for microelectromechanical systems (MEMS) devices. The optimal design of MEMS devices, with a microgyroscope as our device example, has been carried out to find the maximum sensitivity satisfying constraints imposed by functional and geometrical constraints. The optimization algorithm (SA) used is essentially an iterative random search procedure with adaptive moves along the coordinate directions. It permits downhill moves under the control of a probabilistic criterion, thus tending to avoid the first local maxima encountered. The optimization results are verified and validated with the finite element method (FEM) and the boundary element method (BEM) IntelliSuite™ results and measured data. When compared with Microsoft Excel Solver™ (generalized reduced gradient algorithm), our SA-based optimization approach exhibits promising superiority, and finds the global solution easily. There is also good agreement among the numerical computation results generated by the SA algorithm, the simulation results generated by IntelliSuite™, and the measured data.

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MEMS for Portable ApplicationsThe 21 st century requires innovative solutions to meet the ever increasing demand for ultra portable and highly efficient energy technologies. Micro-electro-mechanical Systems (MEMS) have shown significant promise in providing robust, low-cost transduction capabilities. MEMS sensors and actuators are ideally suited for small-scale energy harvesting and power generation applications where overall device dimensions are critical. Furthermore, MEMS technology can also be harnessed for large scale energy applications by augmenting these systems with sensing and actuation capabilities in order to improve energy efficiency and reduce costs. This paper highlights some current MEMS research for energy applications and also explores some areas of the energy industry which might benefit from integration withMEMS.

This work is a continuation of previous investigations aimed at developing an innovative microfabricated air-cooling technology that employs an electrohydrodynamic corona discharge (i.e. ionic wind pump) [1], [2]. This technology enables the miniaturization of cooling systems for next generation electronics. Our single ionic wind pump element consists of two parallel collecting electrodes between which a single emitting tip is positioned. Two-dimensional (2-D) and three-dimensional (3-D) simulations using COMSOL Multiphysics™ are additionally employed to predict the temperature distribution, the flow field, and the heat removal capacity of the device in operation. One such model utilizes a small gap between collector and emitter electrodes and demonstrates an improvement in the COP (coefficient of performance) of a single device. Comparisons are made with experimental temperature data on an actual device. The purpose of this work is therefore to optimize the performance of a single microfabricated ionic wind pump to enable the development of an array of these elements for use in larger-scale heat transfer applications.

This paper presents a method of using smart sensors for continuous detection of vital physiological and physical gait signals that can be relayed to a fall prediction algorithm for predicting an imminent faint fall. A novel MEMS based BP sensor will be discussed briefly. Once an imminent faint fall is detected, fall prevention and injury minimization devices will be activated. A body area network consisting sensor circuits utilizing short range ISM communication will be used to enable signal transmission to a processor which acts as a communication gateway as well. This processor would then encode and send the signals via Bluetooth to designated devices to effect inform caregivers or family members.

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In this paper, a novel backpropagation approximation approach based macromodeling technique for a lateral folded-beam comb-drive micro-resonator is proposed. For static simulations, the results show that the macromodel speeds up simulations by a maximum factor of 291 over the ®nite element approach with less than 1.4% error for small displacements of up to 2 lm. The simulation results also show good agreement with the experimental results. For dynamic simulations, it speeds up simulations by a factor of 456 over the ®nite element approach with less than 2% error for an applied Vac of up to 25 V. The ®ndings also show that the dynamic transient analysis of our device is valid for an applied Vdc of up to 125 V. Similarly, the dynamic simulation results also show quality agreement with the experimental results. IntroductionComputer aided design (CAD) tools based on the ®nite element method (FEM) and the boundary element method (BEM) for microelectromechanical systems (MEMS) have been developing rapidly since the late 1990's [1±3]. These design tools together with the advent of ever more powerful IT tools greatly facilitate the design and development of new devices that integrate sensors and actuators into microsystems. Some examples of computer-aided design software systems developed mainly for MEMS devices include Oyster TM , MEMCAD TM , CAEMEMS TM , SESES TM and IntelliCAD TM [4]. An approach of the CADs based on simulation of physical process is to use a direct geometric description of a process step, and build up a geometric model of a device. The CAEMEMS TM system is explicitly designed to support device optimization [5].The need for models with large number of coupled degrees of freedom (DOFs) is increasing due to the advent of more complex MEMS devices. Generating such models by``hand'' such as simple``hand-generated'' models is inef®cient, dif®cult, error-prone, and even impossible at times. An example is the MEMS system-level modelling technique based primarily on lumped-parameter techniques [6,7]. In this technique, the lumped models of the mechanical devices were ®rst generated by converting the mechanical components (inertial mass, damper and spring) into their equivalent electronic components (inductor, resistor and capacitor respectively). These are then incorporated with a signal conditioning circuitry in a circuit-level simulator. This modeling method is only limited to simple systems, typically single DOF systems. Furthermore, it requires a high degree of user intervention. Besides, the determination of dynamic behaviour via full three-dimensional simulation is computationally very expensive, time consuming and too cumbersome.To overcome these shortcomings, some researchers have created accurate reduced-order dynamical mathematical models that are equivalent to a fully meshed dynamic FE model but in a form that can be used for fast dynamical simulations in the context of a circuit-level or system-level simulation environment. These reducedorder models are often referred to as macromodels. The signi®...