Journal of Radio Electronics. eISSN 1684-1719. 2024. ¹8

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DOI: https://doi.org/10.30898/1684-1719.2024.8.3

 

 

 

PREDICTING THE MAGNITUDE OF ELECTROSTATIC DISCHARGE
INTERFERENCE IN AN ELECTRONIC DEVICE
USING AN ARTIFICIAL NEURAL NETWORK

 

Z.M. Gizatullin, R.R. Mubarakov

 

Kazan National Research Technical University
named after A.N. Tupolev-KAI,
420111, Russia, Kazan, Karl Marx str.10

 

The paper was received May 17, 2024

 

Abstract. Electrostatic discharge is a dangerous source of natural electromagnetic interference to the operation of modern electronic devices. Although measures have been taken to reduce and remove electrostatic discharge from the operating area of electronic devices, the absence of such discharge cannot be guaranteed. Therefore, when designing modern electronic devices, it is necessary to take into account the possibility of such electrostatic discharges in advance and take protective measures. The article proposes a method for predicting the amplitude of interference in the communication line of an electronic device when exposed to an electrostatic discharge on its metal case. The technique is based on the use of an artificial neural network. The technique includes the analysis of significant input parameters that affect the amount of interference in an electronic device; development of an experimental stand for measuring interference; choosing the structure and parameters of a neural network for predicting interference; choosing a training method for an artificial neural network; choosing a metric for assessing the quality of training; normalization of training data; training an artificial neural network using experimental noise data; predicting the amplitude of interference in the communication line of an electronic device when exposed to an electrostatic discharge on its body; where necessary, selecting and implementing electrostatic discharge protection measures. Examples of training an artificial neural network based on experimental data are given. The training was carried out for 572 epochs. For the training and testing sets, the discrepancy between the predicted data and the average measured noise values was 3.61% and 3.95%, respectively. Examples are given of predicting the amplitude of interference when exposed to electrostatic discharge. The results obtained indicate the possibility of practical use of an artificial neural network to solve problems of electromagnetic interference analysis.

Key words: neural network, interference, electrostatic discharge, electronic means, modeling, experiment, forecasting, technique.

Financing: The work was supported by the Kazan National Research Technical University named after A.N. Tupolev Strategic Academic Leadership Program (“PRIORITET–2030”).

Corresponding author: Gizatullin Zinnur Marselevich, zmgizatullin@kai.ru

 

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For citation:

Gizatullin Z.M., Mubarakov R.R. Predicting the magnitude of electrostatic discharge interference in an electronic device using an artificial neural network. // Journal of Radio Electronics. – 2024. – ¹ 8. https://doi.org/10.30898/1684-1719.2024.8.3 (In Russian)