Day 2, April 15 - Posters

Start Date

15-4-2020 1:00 PM

End Date

15-4-2020 3:00 PM

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

SETs are analyzed and characterized through ionizing radiation effects spectroscopy (IRES) and machine learning. Potentially catastrophic radiation-induced errors can be exposed with IRES as it simplifies the identification of transients through statistical analysis of waveform behavior, allowing for the capture of subtle changes in circuit dynamics. Leveraging a k-Nearest Neighbors (KNN) machine learning algorithm with IRES data, the identification of transients is facilitated and makes an on-chip implementation feasible.

Date

4-15-2020

Document Type

posters

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by/4.0/

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Apr 15th, 1:00 PM Apr 15th, 3:00 PM

Detecting and Identifying Single Event Transients using IRES and Machine Learning

SETs are analyzed and characterized through ionizing radiation effects spectroscopy (IRES) and machine learning. Potentially catastrophic radiation-induced errors can be exposed with IRES as it simplifies the identification of transients through statistical analysis of waveform behavior, allowing for the capture of subtle changes in circuit dynamics. Leveraging a k-Nearest Neighbors (KNN) machine learning algorithm with IRES data, the identification of transients is facilitated and makes an on-chip implementation feasible.