High precision frequency source anomaly monitor using Kolmogorov-Arnold Networks

Timely detection of abnormalities in clock sources is a crucial issue in the miniaturization and widespread adoption of high-performance clock sources. This paper investigates two common types of faults that may occur in chip-scale atomic clocks and assesses the feasibility of using a novel neural network, Kolmogorov-Arnold Networks, for real-time monitoring. Experimental results demonstrate that Kolmogorov-Arnold Networks can effectively monitor clock sources with high accuracy. However, further detailed experiments are necessary to validate the specific features and monitoring durations required for optimal performance.