Generic ESD Generator Model using Artificial Neural Network

dc.contributor.authorYousaf, Jawad
dc.contributor.authorJaved, Kamran
dc.contributor.authorGhazal, Mohammed
dc.contributor.authorETAL.
dc.date.accessioned2022-02-15T11:56:20Z
dc.date.accessioned2023-08-19T08:17:31Z
dc.date.available2022-02-15T11:56:20Z
dc.date.available2023-08-19T08:17:31Z
dc.date.issued2021-07
dc.description.abstractDifferent commercial ESD gun models, although complying with the standard ESD waveform requirements in terms of rise time and current values for standard Pellegrini target, produce different ESD waveforms. The variations of the ESD source and target impedance in real-time with the change in the gun model affects the ESD susceptibility compliance testing results and immunity analysis for the estimation of possible ESD failure in a product. This study presents, for the first time, a novel generic ESD generator model using artificial neural network (ANN) based deep learning techniques. The developed deep learning model incorporates the characteristics of the real-time generated ESD waveforms by various commonly used commercial ESD gun models with different target load impedances. The presented model could be used as a generic ESD source for fast ESD susceptibility and immunity testing’s at the design stage of a product using numerical or circuit-analysis-based toolsen_US
dc.identifier.citationYousaf, J., Javed, K., & Ghazal, M. (2021, July). Generic ESD Generator Model using Artificial Neural Network. In 2021 IEEE International Joint EMC/SI/PI and EMC Europe Symposium (pp. 1000-1005). IEEE.en_US
dc.identifier.doihttps://doi.org/10.1109/EMC/SI/PI/EMCEurope52599.2021.9559290
dc.identifier.urihttps://edms.wexl.in/handle/1/2663
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectDeep learningen_US
dc.subjectArtificial neural networksen_US
dc.subjectImmunity testingen_US
dc.subjectElectrostatic dischargesen_US
dc.subjectGeneratorsen_US
dc.subjectReal-time systemsen_US
dc.titleGeneric ESD Generator Model using Artificial Neural Networken_US
dc.title.alternativejournal Articalen_US
dc.typeArticleen_US

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