A Systematic Design Optimization Approach for Multiphysics MEMS Devices Based on Combined Computer Experiments and Gaussian Process Modelling.

Shayaan Saghir,Sohail Iqbal,Amir Hamza,Rana Iqtidar Shakoor,Muhammad Mubasher Saleem,Kashif Riaz

doi:10.3390/s21217242

Abstract

This paper presents a systematic and efficient design approach for the two degree-of-freedom (2-DoF) capacitive microelectromechanical systems (MEMS) accelerometer by using combined design and analysis of computer experiments (DACE) and Gaussian process (GP) modelling. Multiple output responses of the MEMS accelerometer including natural frequency, proof mass displacement, pull-in voltage, capacitance change, and Brownian noise equivalent acceleration (BNEA) are optimized simultaneously with respect to the geometric design parameters, environmental conditions, and microfabrication process constraints. The sampling design space is created using DACE based Latin hypercube sampling (LHS) technique and corresponding output responses are obtained using multiphysics coupled field electro–thermal–structural interaction based finite element method (FEM) simulations. The metamodels for the individual output responses are obtained using statistical GP analysis. The developed metamodels not only allowed to analyze the effect of individual design parameters on an output response, but to also study the interaction of the design parameters. An objective function, considering the performance requirements of the MEMS accelerometer, is defined and simultaneous multi-objective optimization of the output responses, with respect to the design parameters, is carried out by using a combined gradient descent algorithm and desirability function approach. The accuracy of the optimization prediction is validated using FEM simulations. The behavioral model of the final optimized MEMS accelerometer design is integrated with the readout electronics in the simulation environment and voltage sensitivity is obtained. The results show that the combined DACE and GP based design methodology can be an efficient technique for the design space exploration and optimization of multiphysics MEMS devices at the design phase of their development cycle.

Highlights

MEMS accelerometers are miniaturized sensors for measuring constant, time varying, and quasi-static accelerations and have wide applications in the field of automotive industry [1], machine condition monitoring [2,3], shock sensing [4], precision navigation [5], and consumer electronics [6]
One of the solutions reported in the literature is the monolithic integration of three proof masses, each sensing in a specific axis, in a single chip [8,9,10]
We present a design and analysis of computer experiments (DACE) based systematic and efficient design methodology for MEMS in general and MEMS accelerometer in particular by using Latin hypercube sampling (LHS) technique to create a design space with different combinations of geometric design parameters and Gaussian process (GP) based metamodelling for the multi-response optimization

Summary

Introduction

MEMS accelerometers are miniaturized sensors for measuring constant, time varying, and quasi-static accelerations and have wide applications in the field of automotive industry [1], machine condition monitoring [2,3], shock sensing [4], precision navigation [5], and consumer electronics [6] For these applications, one basic requirement is that the MEMS accelerometer must be able to determine the position of a body in space by sensing its acceleration in three axes. This results increased device footprint area and increased packaging cost, and measurement error due to misalignment [7] To resolve these issues, one of the solutions reported in the literature is the monolithic integration of three proof masses, each sensing in a specific axis, in a single chip [8,9,10]. In comparison to multiple proof masses integrated, MEMS accelerometers based on single proof mass for sensing multiple axis acceleration have proved to be an efficient solution for achieving a small device footprint, low cost, and improved performance [11,12,13]

Methods

Discussion

Conclusion