Computing the pull-in voltage of fixed–fixed micro-actuators by artificial neural network

Ak, Cevher; Yıldız, Ali; Akdağlı, Ali; Biçer, Mustafa Berkan

doi:10.1007/s00542-016-3128-4

Cited by 2 publications

(1 citation statement)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, ANN models have been widely used to simulate electrical or mechanical characteristics of different MEMS devices. They have been applied mostly to the models of pull-in voltage [20,21,22] and S parameter [23,24,25,26,27,28] of RF-MEMS (radio frequency MEMS) switches, to resonant frequency [29] and spurious modes [30] of RF-MEMS resonator, and to S parameter [31,32,33] of RF-MEMS phase shifter. Uncertainty analysis is the key technique and research focus in the MEMS optimization.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty analysis of sensitivity of MEMS microphone based on artificial neural network

Liu

Jia

2019

IEICE Electron. Express

View full text Add to dashboard Cite

Due to the uncertainties from manufacturing processes and material properties, MEMS (micro-electro-mechanical system) microphone may exhibit significant variations in their performance compared to the nominal design. The published analytical methods have obvious shortcoming, which cannot meet the needs of both accuracy and efficiency. In order to improve efficiency, the MC (Monte Carlo) simulation based on ANN (artificial neural network) is presented to analyze the uncertainty of sensitivity of polysilicon circular clamped diaphragm microphone. Using the PDS (probabilistic design system) of Ansys software and MC simulation to predict the qualified rate of microphone, the simulated results are 91.2% and 91.4% respectively. The qualified rate of manufactured microphones is 91.5%. In addition, the time-consuming of these two simulations are 10 minutes and about 3000 minutes. This paper also analyzes the change of sensitivity probability densities with the varieties of nominal parameters of diaphragm. The results show that the presented MC simulation with accuracy and high efficiency is an alternative to the traditional methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Uncertainty analysis of sensitivity of MEMS microphone based on artificial neural network

Liu

Jia

2019

IEICE Electron. Express

View full text Add to dashboard Cite

show abstract

A Novel Closed-Form Expression Obtained by Using Differential Evolution Algorithm to Calculate Pull-In Voltage of MEMS Cantilever

Yıldız

2018

J. Microelectromech. Syst.

View full text Add to dashboard Cite

Computing the pull-in voltage of fixed–fixed micro-actuators by artificial neural network

Cited by 2 publications

References 43 publications

Uncertainty analysis of sensitivity of MEMS microphone based on artificial neural network

Uncertainty analysis of sensitivity of MEMS microphone based on artificial neural network

A Novel Closed-Form Expression Obtained by Using Differential Evolution Algorithm to Calculate Pull-In Voltage of MEMS Cantilever

Contact Info

Product

Resources

About