Optimization of Microresistor Beam Performance Using COMSOL Multiphysics and Taguchi Method

Abstract

Microresistor beams are crucial components in microelectromechanical systems (MEMS), with broad applications spanning from sensors to microactuators. Their performance is significantly influenced by their design and material properties, thus necessitating an effective optimization strategy. This study proposes a comprehensive approach to the optimization of microresistor beam performance using COMSOL Multiphysics and the Taguchi method. We utilized COMSOL Multiphysics, a powerful finite element analysis tool, to model and simulate the microresistor beam's behavior under a range of conditions. Through COMSOL, we investigated the effects of various design, material and operational parameters, including applied voltage, heat sink temperature and heat transfer coefficient, on beam's performance. The simulations allowed for a nuanced understanding of the complex interplay between these parameters and their impact on the beam's mechanical and electrical characteristics. The Taguchi method, a statistical design of experiments technique, was then employed to optimize these design parameters. By setting up orthogonal arrays, we could systematically explore the design space and identify the optimal combination of parameters that yielded maximum performance and resilience. In addition, the Taguchi method helped in minimizing the undesirable effects of noise factors, enhancing the robustness of the design. The findings of this study highlight the potential of integrating finite element modeling with the statistical design of experiments for the optimization of MEMS components. Moreover, the study offers a replicable and scalable model that can be employed in the optimization of other MEMS components.Although the focus of this study was on microresistor beams, the methodologies and insights gained are applicable to a wide array of microscale devices. Future research could adapt this approach for other MEMS components and explore its potential in different operational environments. In conclusion, this study presents a novel approach to the optimization of microresistor beams, contributing to the growing body of literature on the design and optimization of MEMS. By coupling finite element modeling with the statistical design of experiments, we offer a robust, efficient, and reliable path to the development of high-performance MEMS.