Modeling approach for distributed RF MEMS phase shifter based on IA-BP neural network

Yang, Guohui; Wu, Qun; Fu, Jiahui; Meng, Fanman

doi:10.1109/icmmt.2010.5525104

Cited by 3 publications

(2 citation statements)

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“…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

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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.

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Section: Introductionmentioning

confidence: 99%

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

Liu

Jia

2019

IEICE Electron. Express

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“…Power amplifiers were analyzed using a combination of several neural networks [15], each network was used for prediction of certain parameter of an amplifier. Radial basis function network was used for modelling an RF MEMS phase shifter [16]. A multi-layer perceptron (MLP) network was used to optimize parameters of a microstrip patch antenna [17].…”

Section: Introductionmentioning

confidence: 99%

Delay Systems Synthesis using Multi-Layer Perceptron Network

et al. 2018

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The aim of this paper is to accelerate development and investigation of the delay systems. The computational time for investigation of particular design of delay system may take from several minutes up to several days. To achieve the required constructional parameters of the system, the iterative calculations usually should be repeated many times. In this paper, an artificial neural network is proposed to be used as the universal approximator for solving mathematical problems of delay system investigation instead of usual analytical and numerical techniques. The application of a multi-layer perceptron is proposed for approximation of solution space with discrete estimates, which were initially received by application of numerical techniques. Different structures of the multi-layer perceptron were tested for approximation. The difference between delay systems synthesis, which was estimated using numerical techniques and trained multi-layer perceptron did not exceed 5% for any of the six design parameter values. The execution time for estimating single delay system was reduced from 240 s to 20 ms. Such fast estimation of design parameters enables performing delay system analysis and design in real time, preserving time for structure visualization in 3D or virtual reality environment.

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