Affiliation:
1. University of California at Berkeley, Berkeley, CA
Abstract
This paper proposes a general architecture for using evolutionary algorithms to achieve MEMS design synthesis. Functional MEMS devices are designed by combining parameterized basic MEMS building blocks together using Multi-objective Genetic Algorithms (MOGAs) to produce a pareto optimal set of feasible designs. The iterative design synthesis loop is implemented by combining MOGAs with the SUGAR MEMS simulation tool. Given a high-level description of the device’s desired behavior, both the topology and sizing are generated. The topology or physical configuration includes the number and types of basic building blocks and their connectivity. The sizing of the designs entails assigning numerical values to parameterized building blocks. A sample from the pareto optimal set of designs is presented for a meandering resonator example, along with convergence plots.
Cited by
24 articles.
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