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-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 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 gamma 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.
翻译:新型纳米技术的出现给辐射环境中设计可靠的电子系统带来了重大挑战。 几类辐射,如全离子化剂量(TID)效应,往往对此类纳米级电子设备造成永久性损害,而目前处理TID的先进技术利用昂贵的辐射加固装置。本文侧重于一种新颖而不同的方法:利用消费者电子水平的机器学习算法,现场可编程门阵列(FPGAs)处理TID效应,并在这些效应停止工作之前对其进行更换。这一条件具有一项研究挑战,即当板板因TID效应而导致完全失灵时,需要预测。我们观察了伽马辐射下FGA板的内部测量结果,并使用了三种不同的异常探测机器的算法,以探测伽马辐照环境中传感器测量中的异常现象。统计结果显示伽马辐照水平与板测量结果之间存在非常重要的关系。 此外,我们的异常检测结果显示,用Radial Bir Bornel的单层支持矢机器的单层支持矢测算器平均回0.95分。此外,所有异常现象都可以在板停止工作之前检测到。