Supervised Deep Learning and Classification of Single-Event Transients

被引：2

作者：

Peyton, T. ^{[1
]}

Carpenter, J. L. ^{[1
]}

Camp, S. ^{[1
]}

Fadul, M. ^{[1
]}

Dean, B. ^{[1
]}

Reising, D. R. ^{[1
]}

Loveless, T. D. ^{[1
]}

机构：

[1] Univ Tennessee, Dept Elect Engn, Chattanooga, TN 37403 USA

来源：

IEEE TRANSACTIONS ON NUCLEAR SCIENCE | 2023年 / 70卷 / 08期

关键词：

Training; Phase locked loops; Convolutional neural networks; Integrated circuit modeling; Data models; Transient analysis; Matched filters; Convolutional neural network (CNN); phase-locked loop (PLL); radiation effects; single-event transients (SETs); supervised machine learning (ML); RADIATION EFFECTS SPECTROSCOPY;

D O I：

10.1109/TNS.2023.3268987

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This article describes a method to detect and classify single-event transients (SETs) 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 (CNNs) were used to classify the spatial dependencies of the transient responses. A clustering technique is described to identify the groups of related circuit nodes and achieves over 90% identification accuracy.

引用

页码：1740 / 1746

页数：7

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