Deep Learning Based Multiresponse Optimization Methodology for Dual-Axis MEMS Accelerometer

Micromachines (Basel). 2023 Apr 4;14(4):817. doi: 10.3390/mi14040817.


This paper presents a deep neural network (DNN) based design optimization methodology for dual-axis microelectromechanical systems (MEMS) capacitive accelerometer. The proposed methodology considers the geometric design parameters and operating conditions of the MEMS accelerometer as input parameters and allows to analyze the effect of the individual design parameters on the output responses of the sensor using a single model. Moreover, a DNN-based model allows to simultaneously optimize the multiple output responses of the MEMS accelerometers in an efficient manner. The efficiency of the proposed DNN-based optimization model is compared with the design of the computer experiments (DACE) based multiresponse optimization methodology presented in the Literature, which showed a better performance in terms of two output performance metrics, i.e., mean absolute error (MAE) and root mean squared error (RMSE).

Keywords: deep learning (DL); deep neural network; dual-axis MEMS accelerometer; microelectromechanical systems (MEMS); multiresponse optimization; neural network.

Grants and funding

This work was supported by the Higher Education Commission of Pakistan and the National Centre of Robotics and Automation under Grant DF 1009-0031.