We present the miniaturization limits of axially loaded piezoresistive MEMS accelerometers. Therefore we identify limiting factors on the basis of FEM-verified analytical models. To ensure a broad discussion we compare two different axially loaded topologies: first a conventional topology, which can be manufactured already today, and second a future-oriented topology utilizing nanowires. To enable a realistic comparison of the different topologies we shrink the sensor while maintaining a specific performance (e.g. sensitivity and noise) considering design limitations such as fracture of silicon and buckling. To find the minimum total sensor area under certain constraints and therefore the optimal geometric and material parameters we apply optimization techniques to our analytical models. It will be seen that the piezoresistive transducer principle for MEMS accelerometers has a promising shrink potential with minimum total sensor dimensions as low as 150 9 150 9 10 lm 3 achievable by use of currently available manufacturing processes.

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.