Cryogenic Hemt Noise Modeling by Artificial Neural Networks

Caddemi, Alina; Catalfamo, F.; Donato, Nicola

doi:10.1142/s0219477505002859

Cited by 14 publications

(5 citation statements)

References 11 publications

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“…This kind of approach overcomes the difficulties of extracting the values of circuit and noise elements which vary according the device type, bias conditions and temperature. Therefore, application of ANN procedures represents an interesting modeling tool but the accessible generalization features strongly depend on the amount and the choice of data employed in the training phase [8,9].…”

Section: Noise Parameters In Microwave Devicesmentioning

confidence: 99%

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An evolution algorithm for noise modeling of HEMT’s down to cryogenic temperatures

2006

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The implementation of an Evolution Algorithm (EA) for the cryogenic noise modeling of microwave devices has been the object of the present work. Several approaches exist to treat the modeling issue as far as advanced microwave transistors are concerned. The aim of such procedures is mostly oriented to extract a linear equivalent circuit including noise performance that allows to reproduce the global device behavior in a small signal regime. In the present paper, the authors show how EA's can be employed to solve the noise modeling problem according to a blackbox approach. The application refers to the simulation of the Noise Parameters of High Electron Mobility Transistors (HEMT) in the 6-18 GHz frequency range and down to cryogenic temperatures (90K) compared with experimental data. The quality of results indicates that EA techniques represent a truly alternative way to determine the microwave noise performance of HEMT devices, thus furnishing a flexible tool to support CAD of high sensitivity -ultra high speed circuits.

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Section: Noise Parameters In Microwave Devicesmentioning

confidence: 99%

“…This approach is original and very flexible because it does not require a training procedure like in the Artificial Neural Networks (ANNs) -based systems [8,9]. It also allows to perform an analysis of the stability performances of the parameters under test.…”

Section: Implementation Of the Evolution Algorithmmentioning

confidence: 99%

An evolution algorithm for noise modeling of HEMT’s down to cryogenic temperatures

2006

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“…Moreover, ANNs have been recently adopted as an interesting modeling tool for the active device characterization in the microwave frequency range. Our most recent works in this field have been focused upon GaAs lattice-matched and pseudomorphic HEMT's previously characterized in our laboratory [5,9]. By implementing these procedures, we have been able to reproduce the small-signal circuit model and noise parameters of several device types from only one measured scattering parameter set, one frequency point and one noise figure value.…”

Section: Artificial Neural Network Modelingmentioning

confidence: 99%

Simulating Noise Performance of Advanced Devices down to Cryogenic Temperatures

Caddemi¹

2005

AIP Conference Proceedings

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The aim of the present work is the development of a suitable neural network structure to compute the microwave noise parameters of High Electron Mobility Transistors (HEMT). The noise parameters (NP) here considered are the magnitude (|Γ opt |) and phase (∠Γ opt ) of the optimum noise source reflection coefficient, the minimum noise figure (F min ) and the noise resistance (R n ). By this procedure, we are able to reproduce the above noise parameters of several device types from only one measured scattering parameter set, one frequency point and one generic noise figure value at different bias and/or temperature conditions. The employed noise figure is that measured in input matched conditions (i.e. 50 Ω source impedance), namely F 50 . The trained network outputs were compared with data of noise measurements performed in our lab and the results have shown to be in a very good agreement. We observed that the difference occurring between the expected value of the noise parameters and the value calculated by the neural network is negligible. The procedure here presented is the most effective choice to avoid the need of any circuit simulations. Our neural network allows great time saving, thus leading to a rapid and complete characterization of the active microwave device under test with a remarkable generalization feature.

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“…It combines the new instrumentation and the engineering knowledge with chemistry and medicine, and it is the attainment of a diagnostic monitoring for human diseases [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

The Applications of the New Technologies “E-Sensing” in Hospitals

Cannistraro¹,

Lorenzini²

2016

IJHT

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The technologies or techniques "e-sensing", in the past decade have had significant developments from the technical and commercial point of view. The term "e-sensing", refers to the ability to reproduce human senses by means of sensors and recognition systems. The proposed work has been developed within the doctoral thesis and is part of a larger research project about sensors underway at the Faculty of Engineering of Messina. The research, combining the knowledge of engineering methods and tools with medical and chemical competence, is oriented to the development of a system able of monitoring the exhaled air for diagnostic purposes, for the realization of electronic systems at a low cost, for the diagnostics in medicine. It was built an artificial olfactory system, known as "electronic nose", which allows monitoring the exhaled air components of patients undergoing a dialysis treatment. This diagnostic non-invasive method is able to provide at low cost, through the specific control of the markers of the exhaled air, some useful information to assess health of dialyzed patients. The realized device, was tested for biomedical applications and mainly for the specific monitoring of volatile biomarkers in the exhaled air, associated with certain medical conditions (eg. NH3 for nephrological diseases). It will allow to obtain an effective early diagnosis of the disease reducing care times and, at the same time, reducing the invasiveness of diagnostic treatments.

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Cryogenic Hemt Noise Modeling by Artificial Neural Networks

Cited by 14 publications

References 11 publications

An evolution algorithm for noise modeling of HEMT’s down to cryogenic temperatures

An evolution algorithm for noise modeling of HEMT’s down to cryogenic temperatures

Simulating Noise Performance of Advanced Devices down to Cryogenic Temperatures

The Applications of the New Technologies “E-Sensing” in Hospitals

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