Hardness assurance testing for proton direct ionization effects
Bibliographic record
Abstract
The potential for using the degraded beam of high-energy proton radiation sources for proton hardness assurance testing for ICs that are sensitive to proton direct ionization effects are explored. SRAMs were irradiated using high energy proton radiation sources (~67-70 MeV). The proton energy was degraded using plastic or Al degraders. Peaks in the SEU cross section due to direct ionization were observed. To best observe proton direct ionization effects, one needs to maximize the number of protons in the energy spectrum below the proton energy SEU threshold. SRIM simulations show that there is a tradeoff between increasing the fraction of protons in the energy spectrum with low energies by decreasing the peak energy and the reduction in the total number of protons as protons are stopped in the device as the proton energy is decreased. Two possible methods for increasing the number of low energy protons is to decrease the primary proton energy to reduce the amount of energy straggle and to place the degrader close to the DUT to minimize angular dispersion. These results suggest that high-energy proton radiation sources may be useful for identifying devices sensitive to proton direct ionization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".