scholarly journals Artifical neural networks in RF MEMS switch modelling

2016 ◽  
Vol 29 (2) ◽  
pp. 177-191 ◽  
Author(s):  
Zlatica Marinkovic ◽  
Vera Markovic ◽  
Tomislav Ciric ◽  
Larissa Vietzorreck ◽  
Olivera Pronic-Rancic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Communication Systems ◽  
Rf Mems ◽  
Mems Switches ◽  
Mems Switch ◽  
Artifical Neural Networks ◽  
Rf Mems Switches ◽  
Artificial Neural ◽  
Similar Accuracy

The increased growth of the applications of RF MEMS switches in modern communication systems has created an increased need for their accurate and efficient models. Artificial neural networks have appeared as a fast and efficient modelling tool providing similar accuracy as standard commercial simulation packages. This paper gives an overview of the applications of artificial neural networks in modelling of RF MEMS switches, in particular of the capacitive shunt switches, proposed by the authors of the paper. Models for the most important switch characteristics in electrical and mechanical domains are considered, as well as the inverse models aimed to determine the switch bridge dimensions for specified requirements for the switch characteristics.

Target Application Based Design Approach for RF MEMS Switches using Artificial Neural Networks

2022 ◽  
Author(s):  
Lakshmi Narayana Thalluri ◽  
Samuyelu Bommu ◽  
Sathuluri Mallikharjuna Rao ◽  
K. Srinivasa Rao ◽  
Koushik Guha ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Rf Mems ◽  
Design Approach ◽  
Mems Switches ◽  
Rf Mems Switches ◽  
Artificial Neural

scholarly journals RF MEMS Switch Fabrication and Packaging

2020 ◽  
Author(s):  
Lakshmi Swaminathan
Keyword(s):  
Communication Systems ◽  
High Performance ◽  
Satellite Communication ◽  
Rf Mems ◽  
Mems Devices ◽  
Micro Electro Mechanical Systems ◽  
Mems Switches ◽  
Mems Switch ◽  
Hermetic Sealing ◽  
Rf Mems Switches

RF (Radio Frequency) MEMS (Micro Electro Mechanical Systems) technology is the application of micromachined mechanical structures, controlled by electrical signals and interacting with signals in the RF range. The applications of these devices range from switching networks for satellite communication systems to high performance resonators and tuners. RF MEMS switches are the first and foremost MEMS devices designed for RF technology. A specialized method for fabricating microsturctures called surface micromachining process is used for fabricating the RF MEMS switches. Die level packaging using available surface mount style RF packages. The packaging process involved the design of RF feed throughs on the Alumina substrates to the die attachment, wire bonding and hermetic sealing using low temperature processes.

scholarly journals Performance Comparison Of Shunt Rf Mems Switches

2021 ◽  
Vol 12 (5) ◽  
pp. 596-606
Author(s):  
S Girish Gandhi, I Govardhani, M Venkata Narayana, K Sarat Kumar
Keyword(s):  
Comparative Study ◽  
Insertion Loss ◽  
Return Loss ◽  
Rf Mems ◽  
Switching Time ◽  
Performance Comparison ◽  
Mems Switches ◽  
Mems Switch ◽  
Rf Mems Switches ◽  
And Performance

This is an attempt to compare three different shunt configured RF MEMS switches which offers a choice for applications in satellite and antennas. Advanced RF communication domain demands for design and modeling of RF MEMS switch which provides extremely reduced pull-in voltage, better isolation, low insertion loss, and with greater reliability. The proposed work manages with comparison of design modeling and performance of three different shunt configured RF MEMS switches. The proposed shunt configured RF MEMS switches are designed with different dimensions with different meandering techniques with perforations on beam structure helps in reducing the amount of voltage required for actuation of switch which is known as pull-in voltage. Comparative study of three different RF MEMS switches which involves in conducting electromechanical analysis are carried out using COMSOL multi physics tool and electromagnetic analysis are carried out using HFSS tool. Moreover the comparative study involves in comparing the values of pull-in voltage, switching time and capacitance, stress, insertion loss, return loss and isolation of three different RF MEMS switches. Proposed first switch model derives pull-in voltage of 16.9v with the switching time of 1.2µs, isolation of 47.70 dB at 5GHz and insertion loss of 0.0865 dB and return loss of 41.55 dB. Proposed second switch model derives pull-in voltage of 18.5v with the switching time of 2.5µs, isolation of 37.20 dB at 8GHz and insertion loss of 0.1177 dB and return loss of 38.60 dB. Proposed third switch model delivers pull-in voltage of 18.75v with the switching time of 2.56µs, isolation of 44.1552 dB at 8GHz and insertion loss of 0.0985 dB and return loss of 42.1004 dB.

Exploring Structural and Dynamical Properties Microtubules by Means of Artificial Neural Networks

2014 ◽  
pp. 191-203
Author(s):  
R. Pizzi ◽  
S. Fiorentini ◽  
G. Strini ◽  
M. Pregnolato
Keyword(s):  
Neural Networks ◽  
Electromagnetic Field ◽  
Artificial Neural Networks ◽  
Communication Systems ◽  
Dynamical Behaviour ◽  
Information Communication ◽  
Cellular Functions ◽  
Dynamical Properties ◽  
Physical Measures ◽  
Artificial Neural

Microtubules (MTs) are cylindrical polymers of the tubulin dimer, are constituents of all eukaryotic cells cytoskeleton and are involved in key cellular functions and are claimed to be involved as sub-cellular information or quantum information communication systems. The authors evaluated some biophysical properties of MTs by means of specific physical measures of resonance and birefringence in presence of electromagnetic field, on the assumption that when tubulin and MTs show different biophysical behaviours, this should be due to their special structural properties. Actually, MTs are the closest biological equivalent to the well-known carbon nanotubes (CNTs), whose interesting biophysical and quantum properties are due to their peculiar microscopic structure. The experimental results highlighted a physical behaviour of MTs in comparison with tubulin. The dynamic simulation of MT and tubulin subjected to electromagnetic field was performed via MD tools. Their level of self-organization was evaluated using artificial neural networks, which resulted to be an effective method to gather the dynamical behaviour of cellular and non-cellular structures and to compare their physical properties.

Exploring Structural and Dynamical Properties Microtubules by Means of Artificial Neural Networks

2013 ◽  
pp. 78-91
Author(s):  
R. Pizzi ◽  
S. Fiorentini ◽  
G. Strini ◽  
M. Pregnolato
Keyword(s):  
Neural Networks ◽  
Electromagnetic Field ◽  
Artificial Neural Networks ◽  
Communication Systems ◽  
Dynamical Behaviour ◽  
Information Communication ◽  
Cellular Functions ◽  
Dynamical Properties ◽  
Physical Measures ◽  
Artificial Neural

Microtubules (MTs) are cylindrical polymers of the tubulin dimer, are constituents of all eukaryotic cells cytoskeleton and are involved in key cellular functions and are claimed to be involved as sub-cellular information or quantum information communication systems. The authors evaluated some biophysical properties of MTs by means of specific physical measures of resonance and birefringence in presence of electromagnetic field, on the assumption that when tubulin and MTs show different biophysical behaviours, this should be due to their special structural properties. Actually, MTs are the closest biological equivalent to the well-known carbon nanotubes (CNTs), whose interesting biophysical and quantum properties are due to their peculiar microscopic structure. The experimental results highlighted a physical behaviour of MTs in comparison with tubulin. The dynamic simulation of MT and tubulin subjected to electromagnetic field was performed via MD tools. Their level of self-organization was evaluated using artificial neural networks, which resulted to be an effective method to gather the dynamical behaviour of cellular and non-cellular structures and to compare their physical properties.

Analysis of Cycle Lifetimes and Failure Modes for RF MEMS Switches

2005 ◽  
Author(s):  
Chris Brown ◽  
Jacqueline Krim ◽  
Art Morris
Keyword(s):  
Failure Modes ◽  
Electrode Materials ◽  
Rf Mems ◽  
Operating Parameters ◽  
Rf Mems Switch ◽  
Testing Conditions ◽  
Mems Switches ◽  
Mems Switch ◽  
Contact Points ◽  
Rf Mems Switches

RF MEMS switch lifetimes are limited by stiction of the moving components and degradation of the metal to metal contact points during cycling. Currently, maximum switch lifetimes are around 10 to 25 billion cycles. Past experimentation has shown that some stiction problems can be overcome by carefully controlling the operating parameters, but problems at the contact points remain [1]. It is believed that by developing a set of tribological design rules which limit the factors leading to catastrophic failure, switches can operate in excess of 100 billion cycles. Recent research has quantified the reliability and durability of gold contact points on RF MEMS switches as a function of current [2]. Most experimentation on RF MEMS switches has focused on controlling the operating parameters such as current, voltage, electrode materials, contact area, switching mode and force; however, limited work has been performed on a single device type in multiple environmentally controlled testing conditions such as vacuum, cryogenic temperatures, etc. This presentation will discuss performance of the wiSpry RF MEMS switch focusing on quantification of device reliability and failure mechanisms under various atmospheric and temperature conditions. Environmental testing conditions include switching in open air, vacuum and inert gasses, in temperatures ranging from 294 K to 4 K.

scholarly journals Experimental Implementation of a Neural Network Optical Channel Equalizer in Restricted Hardware Using Pruning and Quantization

2022 ◽  
Author(s):  
Diego Argüello Ron ◽  
Pedro Jorge Freire De Carvalho Sourza ◽  
Jaroslaw E. Prilepsky ◽  
Morteza Kamalian-Kopae ◽  
Antonio Napoli ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Complexity ◽  
Communication Systems ◽  
Complexity Reduction ◽  
Edge Computing ◽  
Optical Channel ◽  
Raspberry Pi ◽  
Artificial Neural ◽  
The Impact

Abstract The deployment of artificial neural networks-based optical channel equalizers on edge-computing devices is critically important for the next generation of optical communication systems. However, this is a highly challenging problem, mainly due to the computational complexity of the artificial neural networks (NNs) required for the efficient equalization of nonlinear optical channels with large memory. To implement the NN-based optical channel equalizer in hardware, a substantial complexity reduction is needed, while keeping an acceptable performance level. In this work, we address this problem by applying pruning and quantization techniques to an NN-based optical channel equalizer. We use an exemplary NN architecture, the multi-layer perceptron (MLP), and address its complexity reduction for the 30 GBd 1000 km transmission over a standard single-mode fiber. We demonstrate that it is feasible to reduce the equalizer’s memory by up to 87.12%, and its complexity by up to 91.5%, without noticeable performance degradation. In addition to this, we accurately define the computational complexity of a compressed NN-based equalizer in the digital signal processing (DSP) sense and examine the impact of using different CPU and GPU settings on power consumption and latency for the compressed equalizer. We also verify the developed technique experimentally, using two standard edge-computing hardware units: Raspberry Pi 4 and Nvidia Jetson Nano.

Analysis of Topographical Changes and Adhesion Failures in Gold-to-Gold Metal RF MEMS Switches With Cycling

2009 ◽  
Author(s):  
Seung Min Yeo ◽  
Spyros I. Tseregounis ◽  
Andreas A. Polycarpou ◽  
Adam Fruehling ◽  
Dimitrios Peroulis
Keyword(s):  
Contact Area ◽  
Contact Surface ◽  
Rf Mems ◽  
Critical Factor ◽  
Bottom Surface ◽  
Material Transfer ◽  
Mems Switches ◽  
Mems Switch ◽  
Rf Mems Switches ◽  
Gold Contact

Topographical changes within the contact area as a function of cycling could be a critical factor causing failure and reliability issues in RF MEMS switch operation. In this paper, gold-to-gold contact, cantilever-type RF MEMS switches were tested (cold-switching mode) for different number of cycles, namely, 10, 102, 103, 104, 105, and 106. After the cycling tests, the contact area of each switch was scanned using optical microscopy, scanning electron microscopy and atomic force microscopy to quantify the exact gold-to-gold contact surface changes, leading to adhesion failures (at about 106 cycles). Detailed roughness analysis was carried out to better quantify topographical changes on the contact surface and relate them to failures. It was found that the material transfer from the top beam to the bottom substrate was dominant, and observed after only few cycles. Adhesion failure of gold-to-gold contact switches could be attributed to large protrusions formed on the bottom surface as the switch cycles over 105 times.

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