Optimization of RF MEMS switch using linear vector quantization network
LN Thalluri, N Britto Martin Paul, KVV Kumar… - … Devices, Circuits and …, 2022 - Springer
Micro and Nanoelectronics Devices, Circuits and Systems: Select Proceedings of …, 2022•Springer
This paper presents optimization of cantilever-based radio frequency (RF) micro-electro-
mechanical system (MEMS) technology switches using artificial neural network (ANN)-
based prediction algorithms, ie, linear vector quantization network. We have created a
literature survey-based train dataset and finite element method (FEM) simulation-based test
datasets for cantilever structure-based RF MEMS switches. The dataset training is done with
different algorithms, ie, Bayesian regularization and extracted performance indices.
mechanical system (MEMS) technology switches using artificial neural network (ANN)-
based prediction algorithms, ie, linear vector quantization network. We have created a
literature survey-based train dataset and finite element method (FEM) simulation-based test
datasets for cantilever structure-based RF MEMS switches. The dataset training is done with
different algorithms, ie, Bayesian regularization and extracted performance indices.
Abstract
This paper presents optimization of cantilever-based radio frequency (RF) micro-electro-mechanical system (MEMS) technology switches using artificial neural network (ANN)-based prediction algorithms, i.e., linear vector quantization network. We have created a literature survey-based train dataset and finite element method (FEM) simulation-based test datasets for cantilever structure-based RF MEMS switches. The dataset training is done with different algorithms, i.e., Bayesian regularization and extracted performance indices.
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