MEMS optimization incorporating genetic algorithms

Gregory A. Kirkos; Robert P. Jurgilewicz; Stephen J. Duncan

doi:10.1117/12.341234

10 March 1999 MEMS optimization incorporating genetic algorithms

Gregory A. Kirkos, Robert P. Jurgilewicz, Stephen J. Duncan

Proceedings Volume 3680, Design, Test, and Microfabrication of MEMS and MOEMS; (1999) https://doi.org/10.1117/12.341234
Event: Design, Test, and Microfabrication of MEMS/MOEMS, 1999, Paris, France

Abstract

Micromechanical sensors are routinely simulated using finite element software. Once a structure has ben proposed, various parameters are optimized using experience, intuition, and trial-and-error. However, using proven finite element modeling coupled with a genetic algorithm (GA), optimal designs can be 'evolved' using a hands-free approach on a workstation. Once a problem is defined, the sole task required of the designer is the specification of a mathematical objective function expressing the desired properties of the sensor; the sensor geometry that maximizes the given function is then synthesized by the algorithm. We have developed an optimization tool and have applied it to the design of tuning fork gyroscopes (TFG). In this paper, we demonstrate how a TFG was optimized using GA's. TFG suspension beam lengths were adjusted through the robust search technique, which is resistant to trapping in local maxima. Desired vibration mode order and mode frequency separations were governed by the objective function as specified by the designer. This multidimensional nonlinear optimization problem had a solution space of over eight million possible designs. Industry-standard mechanical computer-aided engineering tools were integrate along with a GA toolbox and a web-based control interface. Designs offering reduced vibration sensitivity and increased sensor dynamic range have been produced. A tenfold decrease in total sensor optimization time has been documented, resulting in reduced development time.

Citation Download Citation

Gregory A. Kirkos, Robert P. Jurgilewicz, and Stephen J. Duncan "MEMS optimization incorporating genetic algorithms", Proc. SPIE 3680, Design, Test, and Microfabrication of MEMS and MOEMS, (10 March 1999); https://doi.org/10.1117/12.341234

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