Vera Markovic

An Extensive Experimental Analysis of the Kink Effects in

This paper, for the first time, analyzes in detail the kink phenomenon in S22 as observed in GaN HEMT technology. To gain a comprehensive understanding, the kink effect (KE) is studied with respect to temperature and bias conditions. The achieved results clearly show that the dependence of the KE on the operating condition should be mainly ascribed to the transconductance, which plays a determinant role in the appearance of this effect. Furthermore, the analysis is extended to investigate the peak in the magnitude of h21 showing its disappearance at low drain-source voltage, due to the increase of the intrinsic output conductance. The importance of this investigation originates from the fact that an accurate and complete characterization of these anomalous phenomena enables microwave engineers to properly take them into account during the modeling and design phases.enables microwave engineers to properly take them into account during the modeling and design phases.

{ S}_{22}

A simple procedure for the extraction of intrinsic noise wave temperatures in the wave representations of microwave transistors is presented in this paper. A set of equations describing the noise parameters as a function of three equivalent noise temperatures is implemented within the circuit simulator Libra. After that, the wave noise model is defined as a new user-defined element of the Libra program library. Good agreement between modeled and measured noise parameters is observed.

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Abstract Extraction of parameters of a small-signal model is the first step in modeling transistors for advanced microwave applications. There are different extraction techniques, mostly based on optimizations or on direct analytical procedures. An alternative to the standard extraction methods are procedures based on the application of artificial neural networks. Namely, an artificial neural network is trained to determine equivalent circuit elements directly from the measured scattering parameters without the need for any additional tuning of the elements. In this study the results of a comprehensive analysis of the neural network based extraction procedures are presented. Stability of the extracted values with the choice of the input set of scattering parameters as well as accuracy of the final small-signal model were examined. Moreover, the influence of the number of measured data necessary for development of reliable neural models was investigated. The extraction procedure was examined for a HEMT transistor working under varying temperature conditions.

{ h}_{21}

Todays widespread use of mobile phones has raised the concern about the possible harmful biological effects of long-term exposure to the microwave radiation of low intensity. In vivo investigations of non-thermal influences under the exposure to actual GSM phone radiation comes predominantly from animal studies. In this paper, the results of an experimental exposition of mice by mobile phones are presented. Biological effects of microwave radiation on brain and liver of experimental animals, and increased oxidative stress as a possible pathogenetic mechanism for harmful effects, have been investigated

for a GaN HEMT

In this paper, the artificial neural network approach is proposed for prediction purposes of temperature behavior of microwave transistors. Neural networks are used for modeling of temperature dependencies of elements of transistor small-signal models including noise. These dependencies are extracted from transistor signal and noise data referred to a set of temperatures, The developed models are valid in the whole operational range of temperatures.

MESFET noise modeling based on noise wave temperatures

A procedure for extracting the noise wave sources in the wave representations of microwave transistors is presented in this paper. Standard microwave engineers software tools are used for this purpose. The elements of noise wave correlation matrices are calculated for the device intrinsic circuit which is obtained using a de-embedding procedure. The strength of noise wave sources and the correlation coefficient are shown graphically as a function of frequency and discussed.

Analysis and validation of neural network approach for extraction of small-signal model parameters of microwave transistors

In this paper an automated procedure for prediction of microwave transistor noise parameters versus temperature is presented. It is based on an improved Pospieszalskis noise model. In order to avoid extraction of device noise model equivalent circuit parameters (ECP) from the measured scattering and noise parameters for each operating temperature, an artificial neural network is introduced for modeling of the ECP temperature dependence. Therefore, it is necessary to acquire the measured data and extract the ECP only for several operating temperatures used for the network training. Once the network is trained and assigned to the considered noise model, the device noise parameters are easily obtained for each temperature from the operating range. It is done without changes in the network structure and without the need for time consuming and complex measurements and optimiztions.

The Results of Experimental Exposition of Mice by Mobile Telephones

This paper presents an application of a neural network in the optical character recognition (OCR) system. It introduces general architecture of modern OCR systems, discussing each module in detail. Specific contribution of this paper is novelty of the character extraction and segmentation, by considering them as image features. MSER (Maximally Stable Extremal Regions) feature detector is applied, discussing numerical and practical restrictions for character segmentation and recognition. The neural network is trained for character recognition and applied on the appropriate example.

Implementation of temperature dependence in small-signal models of microwave transistors including noise

Heterojunction bipolar transistor (HBT) technology is very attractive for microwave wireless communications. A small-signal and noise model of HBTs is presented in this paper. Modeling procedure is based on the artificial neural network (ANN) approach, which enables high accuracy together with the efficiency and simplicity commonly requested from CAD techniques. The prediction of device S- and noise parameters over the whole frequency range and over the broad ranges of operating conditions is possible by the developed ANN model.

Extraction of noise wave sources in MESFET wave representations

Low-noise pHEMT transistors, that have excellent performances at microwave frequencies, can be described by their scattering and noise parameters. In this paper, a pHEMT neural model, based on multilayer perceptron neural networks is proposed. The obtained neural models can predict transistors signal and noise performances very efficiently and accurately for a broad range of bias conditions in the operating frequency range.