Supervised Deep Learning and Classification of Single-Event Transients

This article describes a method to detect and classify single-event transients (SET) to determine the originating circuit node impacted by ionizing radiation. SETs were measured via two-photon absorption (TPA) laser excitation on a custom CMOS phase-locked loop (PLL), and Convolutional Neural Networ...

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Bibliographic Details
Published in:IEEE transactions on nuclear science 2023-08, Vol.70 (8), p.1-1
Main Authors: Peyton, T., Carpenter, J. L., Camp, S., Fadul, M., Dean, B., Reising, D. R., Loveless, T. D.
Format: Article
Language:English
Subjects:
Artificial neural networks
Circuits
Classification
Clustering
Convolutional neural network
Convolutional neural networks
Data models
Deep learning
Integrated circuit modeling
Ionizing radiation
Machine learning
Matched filters
Neural networks
Phase locked loop
Phase locked loops
Photon absorption
Radiation effects
Radiation measurement
Single event transients
Spatial dependencies
Supervised machine learning
Training
Transient analysis
Transient response
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Summary:This article describes a method to detect and classify single-event transients (SET) to determine the originating circuit node impacted by ionizing radiation. SETs were measured via two-photon absorption (TPA) laser excitation on a custom CMOS phase-locked loop (PLL), and Convolutional Neural Networks (CNN) were used to classify the spatial dependencies of the transient responses. A clustering technique is described to identify groups of related circuit nodes and achieves over 90% identification accuracy.
ISSN:0018-9499
1558-1578
DOI:10.1109/TNS.2023.3268987