  An increasing number of unhardened commercial-off-the-shelf embedded devices
are deployed under harsh operating conditions and in highly-dependable systems.
Due to the mechanisms of hardware degradation that affect these devices, ageing
detection and monitoring are crucial to prevent critical failures. In this
paper, we empirically study the propagation delay of 298 naturally-aged FPGA
devices that are deployed in the European XFEL particle accelerator. Based on
in-field measurements, we find that operational devices show significantly
slower switching frequencies than unused chips, and that increased gamma and
neutron radiation doses correlate with increased hardware degradation.
Furthermore, we demonstrate the feasibility of developing machine learning
models that estimate the switching frequencies of the devices based on
historical and environmental data.
