Evaluation of proton cross-section prediction tools for deep sub-micron technologies
Bibliographic record
Abstract
Since the 1990s, several tools have been developed to simulate the sensitivity to proton-induced Single Event Upsets (SEU) of electronic devices, by extracting useful information from an existing heavy ion cross-section. The PROFIT or SIMPA semi-empirical models are used routinely by space agencies and the industry to compute SEU rates and avoid additional proton test campaigns, as such tools are standardized in European Radiation Hardness Assurance (RHA) documents (such as the ECSS-Q-ST-60-15C). More recently, the METIS tool was proposed by Airbus as an improved method for computing the SEU cross-section, following a Monte Carlo approach. This study was motivated by the CNES to assess if such tools were still relevant for sub-100 nm scaled memories, as some of them were only developed and validated on older technologies. This paper proposes an evaluation, for 31 highly scaled devices with available heavy ion and proton test data, of different tools for simulating the SEU cross-section and computed rates: PROFIT, SIMPA and METIS (with High and Low precision). A proton test campaign was also performed on a recent proton beamline on two additional references with existing heavy ion test data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".