Multi-Criteria Layout Synthesis of MEMS Devices Using Memetic Computing

Cem Celal Tutum, Zhun Fan

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

This paper introduces a multi-objective optimization approach for layout synthesis of MEMS components. A case study of layout synthesis of a comb-driven micro-resonator shows that the approach proposed in this paper can lead to design results accommodating two design objectives, i.e. simultaneous minimization of size and power input of a MEMS device, while investigating optimum geometrical configuration as the main concern. The major contribution of this paper is the application of memetic computing in MEMS design. An evolutionary multiobjective optimization (EMO) technique, in particular nondominated sorting genetic algorithm (NSGA-II), has been applied to find multiple trade-off solutions followed by a gradient-based local search, i.e. sequential quadratic programming (SQP), to improve the convergence of the obtained Pareto-optimal front. In order to reduce the number of function evaluations in the local search procedure, the obtained non-dominated solutions are clustered in the objective space and consequently, a postoptimality study is manually performed to find out some common design principles among those solutions. Finally, two reasonable design choices have been offered based on manufacturability issues.
Original languageEnglish
Title of host publication2011 IEEE Congress on Evolutionary Computation
PublisherIEEE
Publication date2011
Pages902-908
ISBN (Print)978-1-4244-7835-4
DOIs
Publication statusPublished - 2011
Event2011 IEEE Congress on Evolutionary Computation - New Orleans, LA, United States
Duration: 5 Jun 20118 Jun 2011

Conference

Conference2011 IEEE Congress on Evolutionary Computation
CountryUnited States
CityNew Orleans, LA
Period05/06/201108/06/2011

Keywords

  • Evolutionary muti-objective optimization
  • Local search
  • Knowledge discovery
  • MEMS design

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