Investigation of TID and Dynamic Burn-In-Induced $V_{{T}}$ Shift on RTG4 Flash-Based FPGA

Rezzak, Nadia; Wang, Jih-Jong; Traas, Michael; Zerrouki, Amal; Bakker, Gregory; Xue, Fengliang; Cai, Alex; Hawley, Frank; McCollum, J.; Hamdy, E.

doi:10.1109/tns.2017.2768038

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…The Authors in [22] evaluated a ( 60 C) gamma-ray radiation testing of a space application FPGA, namely the RT4G150 from Microsemi. Microsemi is a qualified manufacturers list (QML)-certified manufacturer of high-reliability FPGAs for space applications, while RTG4 is the 4 th generation family of radiation-tolerant flash-based FPGAs.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…These papers [22]- [24] summarize most related work for TID effects in FPGAs. They either focus on flash-based devices [22], [24] or point out [23] that they could be a promising alternative for radiation-hardened ones. Also, only one paper monitors current and temperature, although it is not the main objective of that paper to correlate or predict the FPGA stop working.…”

Section: Related Workmentioning

confidence: 99%

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Using Machine Learning for Anomaly Detection on a System-on-Chip under Gamma Radiation

Wächter¹,

Kasap²,

Kolozali³

et al. 2022

Preprint

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The emergence of new nanoscale technologies has imposed significant challenges to designing reliable electronic systems in radiation environments. A few types of radiation like Total Ionizing Dose (TID) effects often cause permanent damages on such nanoscale electronic devices, and current state-of-theart technologies to tackle TID make use of expensive radiationhardened devices. This paper focuses on a novel and different approach: using machine learning algorithms on consumer electronic level Field Programmable Gate Arrays (FPGAs) to tackle TID effects and monitor them to replace before they stop working. This condition has a research challenge to anticipate when the board results in a total failure due to TID effects. We observed internal measurements of the FPGA boards under gamma radiation and used three different anomaly detection machine learning (ML) algorithms to detect anomalies in the sensor measurements in a gamma-radiated environment. The statistical results show a highly significant relationship between the γ radiation exposure levels and the board measurements. Moreover, our anomaly detection results have shown that a One-Class Support Vector Machine with Radial Basis Function Kernel has an average Recall score of 0.95. Also, all anomalies can be detected before the boards stop working.

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Section: Related Workmentioning

confidence: 99%