• Journal of Electrical Engineering and Technology
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• 제12권4호
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• pp.1619-1626
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• 2017

Electronic components that are used in high-level radiation environment require a semiconductor device having a radiation-hardened characteristic. In this paper, we proposed a radiation-hardened I-gate n-MOSFET (n-type Metal Oxide Semiconductors Field Effect Transistors) using a layout modification technique only. The proposed I-gate n-MOSFET structure is modified as an I-shaped gate poly in order to mitigate a radiation-induced leakage current in the standard n-MOSFET structure. For verification of its radiation-hardened characteristic, the M&S (Modeling and Simulation) of the 3D (3-Dimension) structure is performed by TCAD (Technology Computer Aided Design) tool. In addition, we carried out an evaluation test using a $Co^{60}$ gamma-ray source of 10kGy(Si)/h. As a result, we have confirmed the radiation-hardened level up to a total ionizing dose of 20kGy(Si).

• 전기학회논문지
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• 제63권9호
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• pp.1248-1252
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• 2014

In this study, we designed a signal processing module using a radiation hardened technology that can be applied to the all measurement sensors inside nuclear power plant containment. Also, for verification that it can be used for high-level radiation environment (Harsh environmental zone inside containment of NPP), we carried out evaluation tests for a designed module using a $Co^{60}$ gamma-ray source up to 12 kGy(Si). And, we had checked radiation hardening level that it has been satisfied up to 12 kGy(Si).

• Nuclear Engineering and Technology
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• 제54권11호
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• pp.3985-3995
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• 2022

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) can cause permanent damages on such nanoscale electronic devices, and current state-of-the-art technologies to tackle TID make use of expensive radiation-hardened 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 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 gamma radiation exposure levels and the board measurements. Moreover, our anomaly detection results have shown that a One-Class SVM with Radial Basis Function Kernel has an average recall score of 0.95. Also, all anomalies can be detected before the boards are entirely inoperative, i.e. voltages drop to zero and confirmed with a sanity check.