On Extra Delays Affecting I/O Blocks of an SRAM-Based FPGA Due to Ionizing Radiation
Bibliographic record
Abstract
This paper aims at characterizing additional delays induced by ionizing radiation in Input/Output Blocks (IOBs) of Static Random-Access Memory Based Field Programmable Gate Arrays (SRAM-Based FPGAs), using measurement techniques based on ring oscillators (ROs). This characterization effort includes experiments performed with proton irradiation at TRIUMF on Xilinx devices (Virtex-5 and Artix-7). Results from these irradiation experiments show that RO period variations, up to 6.2 ns for Virtex-5 and 3.8 ns for Artix-7, could be induced. These results also reveal that the occurrence rate of events (namely delays and breaks) affecting ROs implemented in IOBs is approaching the rate observed when ROs are implemented in the FPGA core, even if the number of configuration bits dedicated to IOBs is significantly lower than for the FPGA core. These radiation test experiments are supported by emulation using similar RO-based measurement techniques and Xilinx SEU Controller as a fault injector. The fault injection experiments allow a better understanding of the behaviour of IOBs affected by additional delays due to configuration bit flips, which in many cases is similar to what can be observed with an incorrect parameter setting. Emulation experiments also reveal that many of the events modifying IOB behaviour are found to require multiple bit fault injection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".