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Modeling of a Smart Nano Force Sensor Using Finite Elements and Neural Networks

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Abstract

The aim of this work is to model and analyze the behavior of a new smart nano force sensor. To do so, the carbon nanotube has been used as a suspended gate of a metal-oxide-semiconductor field-effect transistor (MOSFET). The variation of the applied force on the carbon nanotube (CNT) generates a variation of the capacity of the transistor oxide-gate and therefore the variation of the threshold voltage, which allows the MOSFET to become a capacitive nano force sensor. The sensitivity of the nano force sensor can reach 0.124 31 V/nN. This sensitivity is greater than results in the literature. We have found through this study that the response of the sensor depends strongly on the geometric and physical parameters of the CNT. From the results obtained in this study, it can be seen that the increase in the applied force increases the value of the MOSFET threshold voltage VTh. In this paper, we first used artificial neural networks to faithfully reproduce the response of the nano force sensor model. This neural model is called direct model. Then, secondly, we designed an inverse model called an intelligent sensor which allows linearization of the response of our developed force sensor.

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Acknowledgement

The authors would like to thank the Mechanical Engineering Laboratory of the University of Biskra, Algeria, for their important help and support in developing the numerical models using ANSYS simulator.

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Authors and Affiliations

  1. Advanced Electronics Laboratory, Department of Electronics, University of Batna, Avenue Chahid Boukhlouf Mohamed El Hadi, Batna, 05000, Algeria

    Farid Menacer, Abdelmalek Kadr & Zohir Dibi

Authors
  1. Farid Menacer
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  2. Abdelmalek Kadr
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  3. Zohir Dibi
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Corresponding author

Correspondence to Farid Menacer.

Additional information

Recommended by Associate Editor Xun Chen

Farid Menacer received the B. Eng. degree in electronics control, and the M. Eng. degree in instrumentation from Batna University, Algeria in 1996 and 2011, respectively.

His research interests include neuronal networks, sensors and intelligent system.

Abdelmalek Kadr received the B. Eng. degree in electronics control, and the M. Eng. degrees in instrumentation from Batna University, Algeria in 1995 and 2012, respectively.

His research interests include neuronal networks, sensors, intelligent system and nanotechnology

Zohir Dibi received the B. Sc. degree in electronics engineering from University of Setif, Algeria in 1994, the M. Eng. and Ph. D. degrees from University of Constantine, Algeria in 1998 and 2002, respectively. He has been the head of Electronics Department. He is currently an assistant professor in Department of Electrical and Electronic Engineering and vice-dean of the Faculty of Engineering, Batna University, Algeria.

His research interests include neural networks, sensors, smart sensors, and organic devices.

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Menacer, F., Kadr, A. & Dibi, Z. Modeling of a Smart Nano Force Sensor Using Finite Elements and Neural Networks. Int. J. Autom. Comput. 17, 279–291 (2020). https://doi.org/10.1007/s11633-018-1155-6

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  • DOI: https://doi.org/10.1007/s11633-018-1155-6

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