DAMPING ANALYSIS TO IMPROVE THE PERFORMANCE OF SHUNT CAPACITIVE RF MEMS SWITCH

This paper describes the significance of the iterative approach and the structure damping analysis which help to get better the performance and validation of shunt capacitive RF MEMS switch. The micro-cantilever based electrostatic ally actuated shunt capacitive RF MEMS switch is designed and after multiple iterations on cantilever structure a modification of the structure is obtained that requires low actuation voltage of 7.3 V for 3 µm deformation. To validate the structure we have performed the damping analysis for each iteration. The low actuation voltage is a consequence of identifying the critical membrane thickness of 0.7 µm, and incorporating two slots and holes into the membrane. The holes to the membrane help in stress distribution. We performed the Eigen frequency analysis of the membrane. The RF MEMS switch is micro machined on a CPW transmission line with Gap-Strip-Gap (G-S-G) of 85 µm - 70 µm - 85 µm. The switch RF isolation properties are analyzed with high dielectric constant thin films i.e., AlN, GaAs, and HfO2. For all the dielectric thin films the RF MEMS switch shows a high isolation of -63.2 dB, but there is shift in the radio frequency. Because of presence of the holes in the membrane the switch exhibits a very low insertion loss of -0.12 dB

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Not AvailableAcoustical detection of insects feeding and crawling sounds was used to automatically monitor internal and external grain feeding bruchids in order to assess the growth and density of food legume bruchids (Callosobruchus chinensis and Callosobruchus maculatus) in bulk stored chickpea and green gram. Bruchids hidden inside the grain kernels were detected acoustically through amplification and filtering of their mobility and feeding sounds. The multivariate technique of artificial neural network (ANN) was applied to assess and predict the bruchids’ density in bulk stored legumes. Five levels of bruchids density (0, 5, 10 15 and 20 bruchids per 500 g) were monitored under without insulation and with insulated condition on the basis of formant parameter obtained by analysis of the acoustic sensor data. The K fold validation method with back propagation multilayer perceptron methodology was used for the prediction of bruchids densities. The maximum and minimum values of accuracy (R2) of 0.99, 0.98 and 0.90, 0.89 could be achieved for both bruchids in stored green gram and chickpea under insulation and without insulation for the training and validation dataset, respectively. Least RMSE (0.82 and 0.89) was obtained for C. maculatus in sound insulated stored green gram for training and validation dataset, respectively. The accuracy of prediction and validation of experimental data with low RMSE and high R2 values for both the food legumes indicated that the ANN modeling performed well in predicting bruchids density. Hence it can be concluded that, best prediction was obtained for the C. maculatus for green gram under insulated condition. The results further corroborated that bioacoustic detection technique with ANN provided a reliable and accurate monitoring technique for bruchids. The developed technique can be adopted in large bulk storage grain systems for the selected legumes for predicting and assessing the growth of bruchids thereby leading to safer storage.Not Availabl