Abstract
We report on the automated determination of the minimal required area of a MEMS accelerometer conforming to given specifications. For a realistic nonlinear sensor model this process is only possible by the use of numerical optimization, which typically has the difficulty of finding the global minimum or is time consuming. A miniaturized sensor’s chip size reduces manufacturing cost and leads to more competitive package sizes and new, unforeseen applications. Size reduction is especially important for consumer applications like mobile phones and navigation devices, where an increasing demand for accelerometers is expected in the near future. With further miniaturization of a sensor it is increasingly important to find the optimal design in order to use chip area as efficiently as possible. To achieve a robust and flexible automated area reduction without loss of functionality we uniquely combine available genetic and gradient-based optimization algorithms. Furthermore, we reduce the model complexity, apply different scaling techniques and adapt optimization algorithm settings. The application to a capacitive and a piezoresistive MEMS accelerometer shows significant improvement of efficiency when compared with the use of currently available optimization algorithms.
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Engesser, M., Franke, A.R., Maute, M. et al. A robust and flexible optimization technique for efficient shrinking of MEMS accelerometers. Microsyst Technol 16, 647–654 (2010). https://doi.org/10.1007/s00542-009-0973-4
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DOI: https://doi.org/10.1007/s00542-009-0973-4