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Research Papers

Case-Based Reasoning for Evolutionary MEMS Design

[+] Author and Article Information
Corie L. Cobb

 Palo Alto Research Center (PARC), 3333 Coyote Hill Road, Palo Alto, CA 94304ccobb@parc.com

Alice M. Agogino

Department of Mechanical Engineering, University of California at Berkeley, 6102 Etcheverry Hall, Berkeley, CA 94720-1740agogino@berkeley.edu

J. Comput. Inf. Sci. Eng. 10(3), 031005 (Sep 01, 2010) (10 pages) doi:10.1115/1.3462920 History: Received July 09, 2009; Revised February 12, 2010; Published September 01, 2010; Online September 01, 2010

A knowledge-based computer-aided design tool for microelectromechanical systems (MEMS) design synthesis called case-based synthesis of MEMS (CaSyn-MEMS) has been developed. MEMS-based technologies have the potential to revolutionize many consumer products and to 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 accelerate the design process. To enable knowledge reuse, a hierarchical MEMS case library has been created. A reasoning algorithm retrieves cases with solved problems similar to the current design problem. Focusing on resonators as a case study, case retrieval demonstrated an 82% success rate. Using the retrieved cases, approximate design solutions were proposed by first adapting cases with parametric optimization, resulting in a 25% reduction in design area on average while bringing 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 MOGA. CBR provided MOGA with good starting points for optimization, allowing efficient convergence to higher quantities of Pareto optimal design concepts while reducing design area by up to 43% and meeting frequency goals within 5%.

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Copyright © 2010 by American Society of Mechanical Engineers
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Figures

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Figure 1

(a) MEMS design process; (b) MEMS design process with case-based design synthesis

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Figure 3

Case retrieval flowchart

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Figure 4

MOGA process with initial population seeded by CBR

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Figure 5

Comparison with MOGA results generated by Kamalian (3) and Zhang (41)

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Figure 6

Comparison of quantity of Pareto optimal solutions generated

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Figure 7

Comparison of best minimum area

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Figure 8

CaSyn-MEMS resonator designs

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