Olivera Pronić

MESFET noise modeling based on noise wave temperatures

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.

Extraction of noise wave sources in MESFET wave representations

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.

Artificial neural networks for temperature dependent noise modeling 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.

Microwave transistors noise modeling by using variable noise wave temperatures

In this paper we proposed a new noise modeling procedure of microwave transistors. The noise modeling is based on a wave representation of transistor intrinsic circuit. The variable noise wave temperatures are introduced as empirical parameters of the model and their frequency dependence is modeled using polynomial regression. In that way noise temperatures can be computed for any frequency from desired frequency range yielding modeling of noise parameters with very good accuracy.

Wave approach to S-parameter and noise parameter prediction of FET devices

This paper presents a simple procedure for the extraction of scattering parameters and noise parameters of microwave transistors. A set of equation describing transistor noise parameters as the function of three equivalent noise wave temperatures is implemented within the circuit simulator Libra. The prediction of noise parameters for a broad frequency range is done on the basis of a single frequency noise parameter measurement. The procedure is applied to FET package models. Good agreement between modeled and measured noise parameters is observed.

Bias-Dependent Model of Microwave FET S-parameters Based on Prior Knowledge ANNs

The applications of artificial neural networks (ANNs) in bias-dependent modeling of S-parameters of microwave FETs have been proposed earlier. Here, a model based on an ANN with additional prior knowledge at its inputs (PKI ANN) is introduced. S-parameters of the device that belongs to the same class as the modeled device are used as the prior knowledge. The PKI concept allows ANN model to be developed with less training data, which is very advantageous when training data is expensive or time consuming to obtain. The proposed modeling concept is illustrated by an appropriate modeling example

Numerically efficient Green’s function for planar structures on multilayered dielectric in cylindrical enclosure

A convenient form of spatial‐domain Green’s function for a point charge in multilayered dielectric medium enclosed by a conducting cylinder of circular cross‐section is presented in this paper. Green’s function expression is obtained by solving Poisson differential equation in cylindrical coordinates and applying analogies with multistep electrical transmission lines. Convergence of the proposed expression obtained in the form of a double infinite sum is investigated and compared with triple‐sum solution for the same problem obtained by standard variable separation method. The numerical investigation has shown that the proposed expression has much faster convergence than the standard solution. Also, contrary to the variable separation method, increasing the number of dielectric layers is not an obstacle in determining of proposed Green’s functions, as it is shown for the three layers dielectric structure.

A wave approach to signal and noise modeling of dual-gate MESFET

A new procedure for signal and noise modeling of dual-gate MESFET is described. The small-signal model is based on two cascaded single-gate MESFET intrinsic equivalent circuits embedded in a network representing device parasitics. Wave interpretation of noise is used for defining the noise parameters of each single gate MESFET. On the basis of that, a CAD-oriented procedure is developed for extracting dual-gate MESFET model parameters as well as noise wave temperatures. Modeled S and noise parameter characteristics are compared to measured ones and quite good agreement is observed.

Frequency dependence of the equivalent gate and drain noise temperatures of microwave transistors

The equivalent gate and drain noise temperatures of microwave FETs in terms of equivalent circuit elements, noise parameters and frequency are considered in this paper. The corresponding relationships are derived and a calculation procedure using circuit simulator Libra is developed. The frequency-dependent gate and drain temperatures are used for the transistor noise parameters modeling.

Bias dependent scalable noise models of MESFETs/HEMTs based on neural networks

A bias-dependent scalable microwave MESFET/HEMT noise model is proposed in this paper. It is based on a multilayer perceptron neural network that produces noise parameters at its outputs for device gate width, biases and frequency presented at its inputs. In that way determination of the noise parameters is enabled for various values of gate width and for all operating points over a wide frequency range. Once the network is trained its structure remains unchanged. After the network training, the noise parameters determination is done without additional optimizations and without need for the measured data that are required for the network training only.