Effects of Total Dose Irradiation on Single-Event Upset Hardness
Bibliographic record
Abstract
The effect of total dose on SEU hardness is investigated as a function of temperature and power supply voltage to determine worst case hardness assurance test conditions for space environments. SRAMs from six different vendors were characterized for single-event upset (SEU) hardness at proton energies from 20 to 500 MeV and at temperatures of 25 and 80degC after total dose irradiating the SRAMs with either protons, Co-60 gamma rays, or low-energy x-rays. It is shown that total dose irradiation and the bias configuration during total dose irradiation and SEU characterization can substantially affect SEU hardness for some SRAMs. For one SRAM, the bias configuration made more than two orders of magnitude difference in SEU cross section at the highest total dose level examined. In addition, it is shown that increasing the temperature during SEU characterization can also increase the effect of total dose on SEU hardness. As a result, worst-case SEU hardness assurance test conditions are the maximum total dose and temperature of the system environment, and the minimum operating voltage of the SRAM. In contrast to previous works, our results using selective area x-ray irradiations show that the source of the effect of total dose on SEU hardness is radiation-induced leakage currents in the memory cells. The increase in SEU cross section with total dose appears to be consistent with radiation-induced currents originating in the memory cells affecting the output bias levels of bias level shift circuitry used to control the voltage levels to the memory cells and/or due to the lowering of the noise margin of individual memory cells caused by radiation-induced leakage currents.
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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 | 0.001 |
| 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".