Evolutionary Computing and Optimal Design of MEMS

Abstract

Fostered by the development of new technologies, microelectromechanical systems (MEMS) are massively present on board of vehicles, within information equipment as well as in medical and healthcare equipment. A smart approach to the design of MEMS devices is in terms of the simultaneous optimization of multiple objective functions subject to a set of constraints. This leads to the family of solutions minimizing the degree of conflict among the objectives (Pareto front). Accordingly, in this paper, a procedure of optimal shape design of MEMS based on evolutionary computing is proposed and validated on three case studies.Index Terms-Comb-drive electrostatic microactuator, electrothermo-elastic microactuator, field analysis and synthesis, finiteelement method, magnetic micromirror, microelectromechanical systems (MEMS), multiobjective optimal shape design.

Multiphysics field analysis and evolutionary optimization: Design of an electro-thermo-elastic microactuator

“…More recently, however, the techniques of actuation most performing in terms of position control fall in three categories: piezoelectric, thermal or magnetic actuation, respectively [3] and [8]. In the paper, reference is made to a thermally actuated device because it is a clear example of a multiphysics domain which asks for a coupledfield model.…”

“…In particular, the optimal design of a class of MEMS has been solved successfully with both evolutionary algorithms like e.g. in [8] and [9] and cooperative algorithms like e.g. the biogeography-based optimization algorithm as shown in [10] and [11] or the wind-driven optimization as shown in [12].…”

Biogeography-inspired multiobjective optimization for helping MEMS synthesis

“…In [13] the magnetic field in a permanent-magnet spherical motor at no-load is recovered, after inverting the magnetic induction measured along an accessible surface; the final aim is to compute the on-load torque by means of the Lorentz's law. In [14] an automated procedure of optimal shape design for MEMS was presented: it exploits finite-element analysis (FEA) for simulating the field in the device, and non-dominated genetic algorithm for trading off multiple objective functions.…”

A Fast Multiobjective Optimization Strategy for Single-Axis Electromagnetic MOEMS Micromirrors

“…Consequently, structured, Computer-Aided-Design (CAD) assisted approaches to MEMS/MOEMS design optimization would be welcome. There is, however, evidence that MEMS design is still largely based on inherently suboptimal ad-hoc, trial-and-error methods [ 16 , 17 , 18 , 19 ]. Part of this problem can be attributed to the lower maturity of the MEMS field with respect to other fields of engineering, which also reflects in a lack of efficient integrated tools for optimal design.…”

“…While compact analytical models are, of course, ubiquitous in MEMS design, as in any other engineering field, numerical methods (and, prominently, Finite Element Modeling) are typically used for design refinement and, crucially, for systematic optimization. For example, Di Barba and Wiak [ 16 ] presented an approach based on a FEM solution of the field distribution (electric or magnetic field) implemented in Comsol Multiphysics, and a genetic algorithm (NSGA-II) for MO. Three different case studies (a comb-finger electrostatic actuator, a torsional magnetic mirror based on a different concept than the one studied in this work, and a Joule thermal actuator) are analyzed.…”