@proceedings {64961,
	title = {Simulation and analysis of Graphene-based nanoelectronic circuits using ANN method},
	journal = {Part of special issue: 14th International Conference on Nanosciences \& Nanotechnologies (NN17), 4-7 July, 2017},
	year = {2018},
	month = {2018},
	abstract = {
	The Graphene{\textendash}based Double Gate Field-Effect Transistors (G-DG FETs) have received great attention in recent years due to their high electrical performance provided for analog and Radio-frequency (RF) nanoelectronic applications. To calculate accurately the drain current and the Figures-of-Merit (FoMs) of the nanoscale G-DG FETs requires the solution of Schr{\"o}dinger/Poisson equations, assuming the quantum effects are to be fully accounted. However, for nanoelectronic circuit simulation, the 2D numerical solution through the fully self-consistent coupled Schr{\"o}dinger/Poisson equations is an overkill approach in terms of both complexity and computational time cost. Hence, new approach and simulation tools which can be applied to design and simulate Graphene-based nanoelectronic circuits are required to overcome the limitations imposed by the accuracy and computational time cost. In this paper, we investigate the efficiency of a new approach based on the ANN-based computation (Artificial neural network) to analyze and simulate Graphene-based nanoelectronic circuits. In this context, this work presents the applicability of ANN for the simulation of the voltage amplifier by investigating the impact of the G-DG FET design parameters on the analog and RF performances. The ANN-based model can be easily implemented into commercial circuit simulators like: SPICE, Cadence and Silvaco.

},
	url = {https://www.sciencedirect.com/science/article/pii/S2214785318309106},
	author = {Kadri Abdelmalek and Menacer Farid and DJEFFAL Fay{\c c}al}
}
@proceedings {64419,
	title = {Modeling and investigation of smart capacitive pressure sensor using artificial neural networks},
	journal = {6th International Conference on Systems and Control (ICSC)},
	year = {2017},
	month = {2017},
	address = {Batna, Algeria},
	abstract = {
	In this paper, a new capacitive pressure sensor (CPS) is investigated and modeled by means of neural approach. The sensing principle in our pressure sensor is based on the determination of the change in the capacity induced by the applied pressure. A ring oscillator is used to convert the capacity variation of the pressure sensor to an output frequency. A multilayer perceptron neural network is used to predict the applied pressure which causes a variation of the capacity including the temperature effects. This model is implemented as an electronic device into PSPICE simulator library, where the device should reproduce faithfully the pressure sensor behavior. Moreover, a new inverse model called smart sensor has been developed, in order to remove the nonlinearity behavior of sensor response. The obtained results make the proposed smart sensor as a potential alternative for high performances pressure sensing applications.

},
	author = {Menacer Farid and Kadri Abdelmalek and DJEFFAL Fay{\c c}al and Dibi Zohir}
}
