Magnetic actuators can be divided into two types: those in which motion changes the gap separation (type I) and those in which motion changes the gap overlap area but not the gap separation (type II). In conventional magnetic actuators of both types, it is assumed that most of the magnetic energy is stored in the gap due to the large reluctance of the gap compared with the negligibly small reluctance of the magnetic core. However, in magnetic microactuators, the fabrication limitations on the achievable cross-sectional area of the magnetic core as well as the finite core permeability increase the core reluctance to the point that this assumption may no longer be valid. In this case, the magnetic energy is distributed in both the gap and the magnetic core, in which the energy distribution is in proportion to the reluctance of the gap and the reluctance of the core respectively. Using an elementary structure of a magnetic actuator, it is shown that for type I microactuators, when the initial gap of the actuator is fixed (e.g., determining the stroke of the actuator), the generated magnetic force has maximum value when the gap overlap area is designed such that the reluctance of the gap is equal to the reluctance of the magnetic core (i.e., ). For type II actuators, the initial overlap area of the actuator is fixed (determining the stroke); therefore the generated magnetic force has a maximum value when the gap separation is designed such that the above equality holds. This paper details both analytical and finite element method (FEM) analysis confirmation for type I actuators. Extension to type II actuators is straightforward.

Metal-organic chemical vapor deposition (MOCVD)is an important technique for growing thin films with various applications in electronics and optics. The development of accurate and efficient MOCVD process models is therefore desirable, since such models can be instrumental in improving process control in a manufacturing environment. This paper presents a semiempirical MOCVD model based on "hybrid" neural networks. The model is constructed by characterizing the MOCVD of titanium dioxide (TiO 2 ) films through the measurement of deposition rate over a range of deposition conditions by a statistically designed experiment in which susceptor and source temperature, flow rate of the carrier gas for the precursor and chamber pressure are varied. A modified backpropagation neural network is then trained on the experimental data to determine the value of the adjustable parameters in an analytical expression for the TiO2 deposition rate. In so doing, a general purpose methodology for deriving semi-empirical neural process models which take into account prior knowledge of the underlying process physics is developed.

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