Case-based reasoning for MEMS design synthesis

A knowledge-based computer-aided design tool for microelectromechanical systems (MEMS) design synthesis called CaSyn-MEMS (Case-based Synthesis of MEMS) has been developed. MEMS-based technologies have the potential to revolutionize many consumer products and create new market opportunities in areas such as biotechnology, aerospace, and data communications. However, the commercialization of MEMS still faces many challenges due to a lack of efficient computer-aided design tools that can assist designers during the early conceptual phases of the design process. CaSyn-MEMS combines a case-based reasoning (CBR) algorithm and a MEMS case library with parametric optimization and a multi-objective genetic algorithm (MOGA) to synthesize new MEMS design topologies that meet or improve upon a designer's specifications.;CBR is an artificial intelligence methodology that uses past design solutions and adapts them to solve current problems. Having the ability to draw upon past design knowledge is advantageous to MEMS designers, allowing reuse and modification of previously successful designs to help deal with the complexities of a current design problem and speed up the design process. To enable knowledge reuse, a hierarchical MEMS ontology has been created, and MEMS designs and sub-assemblies were stored as building blocks in an indexed case library. Reasoning algorithms found cases in the library with solved problems similar to the current design problem. Case retrieval over resonators demonstrated an 82% success rate. Even with a limited case base, micromechanical filters demonstrated a 45% retrieval success rate.;Using the retrieved cases, approximate solutions were proposed by first adapting cases with parametric optimization. An experiment with resonators demonstrated that parametric optimization reduced design area by 25% on average and brought designs within 2% of the frequency goal. In situations where parametric optimization was not sufficient, a more radical design adaptation was performed through the use of a multi-objective genetic algorithm (MOGA). CBR provided MOGA with good starting points for optimization, allowing efficient convergence to higher quantities of optimal design concepts. Design area was also significantly reduced by up to 43%, while meeting frequency goals within 5%.;Providing MEMS designers with a vast array of optimal design concepts increases the chances that good designs will propagate down to the later stages of a MEMS product development cycle, reducing overall development time by avoiding costly fabrication and production processes with sub-optimal design concepts.