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Studying the Degradation of Propagation Delay on FPGAs at the European XFEL

Leandro Lanzieri, Lukasz Butkowski, Jiri Kral, Goerschwin Fey, Holger Schlarb, Thomas C. Schmidt

TL;DR

This paper addresses the degradation of propagation delay in commercially deployed FPGAs within a harsh accelerator environment. It presents an online propagation-delay measurement module using ring-oscillator sensors to monitor aging effects on 298 naturally-aged FPGAs at the European XFEL, compared against unused baselines. The authors demonstrate that operating devices exhibit slower switching correlated with radiation exposure and radiation-dose quartiles, and they validate the feasibility of regression models (e.g., XGBoost, HGB) to estimate switching frequencies from environmental and health data, achieving MAE around 3–5% and R^2 up to ~0.61. The work enables predictive maintenance and real-time degradation assessment for large-scale, radiation-prone FPGA deployments, with implications for reliability in high-dependability systems.

Abstract

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.

Studying the Degradation of Propagation Delay on FPGAs at the European XFEL

TL;DR

This paper addresses the degradation of propagation delay in commercially deployed FPGAs within a harsh accelerator environment. It presents an online propagation-delay measurement module using ring-oscillator sensors to monitor aging effects on 298 naturally-aged FPGAs at the European XFEL, compared against unused baselines. The authors demonstrate that operating devices exhibit slower switching correlated with radiation exposure and radiation-dose quartiles, and they validate the feasibility of regression models (e.g., XGBoost, HGB) to estimate switching frequencies from environmental and health data, achieving MAE around 3–5% and R^2 up to ~0.61. The work enables predictive maintenance and real-time degradation assessment for large-scale, radiation-prone FPGA deployments, with implications for reliability in high-dependability systems.

Abstract

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.
Paper Structure (20 sections, 4 equations, 8 figures)

This paper contains 20 sections, 4 equations, 8 figures.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Ageing Mechanisms on Transistors
  4. Bias Temperature Instability
  5. Hot Carrier Injection
  6. Time-Dependent Dielectric Breakdown
  7. Total Ionizing Dose
  8. Ring Oscillator Sensors on FPGAs
  9. Measurement Method
  10. Procedure Overview
  11. The Propagation Delay Measurement Module
  12. Radiation Environment at the European XFEL
  13. Training of Machine Learning Models
  14. Measurement Results
  15. Hardware Under Study
  16. ...and 5 more sections

Figures (8)

  • Figure 1: Procedure to perform propagation delay measurements and estimations on deployed FPGA devices by means of an online non-concurrent self-test firmware.
  • Figure 2: Diagram of a ring-oscillator-based propagation delay sensor.
  • Figure 3: Propagation delay module consisting of ring oscillators and associated counters, managed by a control unit. PCIe registers provide users with access to the control unit and the measurement results.
  • Figure 4: Frequency distribution the ring oscillator measurements on operational devices.
  • Figure 5: Cumulative distribution functions of the frequency measurements from used and unused devices.
  • ...and 3 more figures