Design and optimisation of one-port RF MEMS resonators and related integrated circuits using ANN-based macromodelling approach

Lee, Y.; Park, Yong-Hwa; Niu, Feng; Filipović, Dejan S.

doi:10.1049/ip-cds:20045211

Cited by 13 publications

(9 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To address this problem, off-line surrogate model-based EAs (SAEAs) are introduced into MEMS shape optimization [21]. An initial random sampling (such as using Design of Experiment [22] methods) is firstly carried out.…”

Section: Introductionmentioning

confidence: 99%

Efficient Design Optimization of High-Performance MEMS Based on a Surrogate-Assisted Self-Adaptive Differential Evolution

et al. 2020

View full text Add to dashboard Cite

High-performance microelectromechanical systems (MEMS) are playing a critical role in modern engineering systems. Due to computationally expensive numerical analysis and stringent design specifications nowadays, both the optimization efficiency and quality of design solutions become challenges for available MEMS shape optimization methods. In this paper, a new method, called self-adaptive surrogate model-assisted differential evolution for MEMS optimization (ASDEMO), is presented to address these challenges. The main innovation of ASDEMO is a hybrid differential evolution mutation strategy combination and its self-adaptive adoption mechanism, which are proposed for online surrogate model-assisted MEMS optimization. The performance of ASDEMO is demonstrated by a high-performance electro-thermoelastic micro-actuator, a high-performance corrugated membrane micro-actuator, and a highly multimodal mathematical benchmark problem. Comparisons with state-of-the-art methods verify the advantages of ASDEMO in terms of efficiency and optimization ability.INDEX TERMS MEMS design optimization, high-performance MEMS design, surrogate model assisted evolutionary algorithm, Gaussian process, differential evolution.

show abstract

Section: Introductionmentioning

confidence: 99%

Efficient Design Optimization of High-Performance MEMS Based on a Surrogate-Assisted Self-Adaptive Differential Evolution

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Dejan Filipovic and his colleagues did some research on RF MEMS switch and resonator modeling using ANN [6] [7]. It is necessary to model the RF MEMS phase shifter which is more complex compare to the switch to make the design more efficient.…”

Section: Introductionmentioning

confidence: 99%

An efficient modeling technique for RF MEMS phase shifter based on RBF neural network

Yang

et al. 2008

2008 International Conference on Microwave and Millimeter Wave Technology

View full text Add to dashboard Cite

A modeling technique based on RBF neural network is presented for the design of RF MEMS phase shifter. Three sensitive parameters are selected according to complicated threedimensional structure design of an RF MEMS phase shifter and used as inputs of neural network. Experiments show that the proposed approach in this paper is a high efficiency modeling for the RF characteristics analysis for RF MEMS phase shifter. The training of the RBF neural network is accomplished within 30 minutes using 27*51 samples. The trained RBF neural network is able to predict the outputs for 51 test samples within 1 minute. Comparison between RBF neural network predictions and HFSS simulations show that the root mean square relatively errors, mean absolute relatively errors and maximize absolute relatively errors are less than 0.0368, 0.0417 and 0.0442 respectively.

show abstract

“…For the up-state position, a total of 45 training and 15 testing datasets are generated while 15 training and 5 testing sets are generated for the down-state position. The ANN architecture is selected following the same procedure as in [21]. For the up-state position, it consists of two-hidden layers with 6 neurons per each layer while two-hidden layers with 3 neurons per each layer are selected for the down-state position.…”

Section: Ann Modelmentioning

confidence: 99%

“…Most importantly, the fast design, analysis and optimization on a device, circuit, and system level with fully compatible multiphysics models of RF MEMS switches are now feasible. Blackbox modules integration starts with extraction of optimized weight and bias matrices of the ANN model [21]. The input variables are passed to the user-defined model subroutine in the circuit simulator which interfaces the model with the simulator solver engine.…”

Section: Integration Of the Ann Modelmentioning

confidence: 99%

Efficient multiphysics modeling of microelectromechanical switches

Lee¹,

Filipović²

2007

The International Journal of Multiphysics

View full text Add to dashboard Cite

Design and optimisation of one-port RF MEMS resonators and related integrated circuits using ANN-based macromodelling approach

Cited by 13 publications

References 18 publications

Efficient Design Optimization of High-Performance MEMS Based on a Surrogate-Assisted Self-Adaptive Differential Evolution

Efficient Design Optimization of High-Performance MEMS Based on a Surrogate-Assisted Self-Adaptive Differential Evolution

An efficient modeling technique for RF MEMS phase shifter based on RBF neural network

Efficient multiphysics modeling of microelectromechanical switches

Contact Info

Product

Resources

About