Sequential Design Method for Geometric Optimization of an Electrothermal Microactuator Based on Dynamic Kriging Models

Abstract

This paper proposes a sequential optimization methodology for designing an electrothermal polysilicon actuator in the presence of a fabrication tolerance. In the proposed method, a deterministic optimum is first sought from an initial design, and then a reliability-based robust design is obtained launching at the deterministic point. This serial design strategy can enhance numerical efficiency through minimizing the use of computationally expensive reliability-based design optimization. To effectively perform the robust design of very complex multiphysics problems, elaborate surrogate models based on the local window concept are exploited comprehensively. The proposed method is applied to an electrothermal polysilicon actuator with seven design random variables, and then three different nominal designs are examined in terms of maximum deflection, consumed power, and confidence level.

Closed-form modelling and design analysis of V- and Z-shaped electrothermal microactuators

“…Electrothermal microactuators have been widely used in designs of microelectromechanical systems (MEMS) for various applications due to their virtues of compatibility, stability, and capabilities in producing high forces and large displacements at lower actuation voltages [1][2][3][4][5]. Among various configurations of the electrothermal actuators, the U-shaped [6,7], V-shaped [8][9][10], and Z-shaped [11,12] types are three typical types to produce in-plane motions. The U-shaped actuator utilizes asymmetrical thermal expansions of two beams with different cross-sectional areas to produce circular arc displacements, whereas the V-shaped and Z-shaped ones can generate rectilinear displacements due to their symmetrical beam structures.…”

Double-objective optimization of electro-thermal (E-T) micro-actuator for fiber switch

“…The remaining sizes are constant values. According to the optimization done in the work [14][15][16][17][18], these four size variables mainly affected the two optimal goals. In order to reduce the time cost and produce a reasonable Ushaped actuator, these four variables (µm) are bounded as follows:…”

“…Nevertheless, the error between the simplified and complete model cannot be neglected, or the credibility of ultimate result can be reduced. Dynamic kriging, another complicate gaussian regression interpolation method, is presented in [18]. In this research, the surrogate model based on dynamic kriging and local window concept was exploited to realize robust design of U-shaped actuator.…”

“…Although many factors including stress, volume, heat and deflection are considered, the calculation model for force is still lacking of accuracy. From these researches above, the shortcomings are summarized as follows: (a) less precision in numerical calculation model for displacement or force of the actuator used in some researches [15,16,19], which directly decreases the credibility of the optimization, (b) defect of the search methods applied in the optimization [14,15], which brings the difficulty of getting ideal result, (c) only the single objective considered in most work [18], which is not fit with its application.…”