Comparative Study on the “Soft Errors” Induced by Single-Event Effect and Space Electrostatic Discharge
Bibliographic record
Abstract
Single event effect (SEE) and space electrostatic discharge (SESD) are two important types of effects causing spacecraft anomalies. However, it is difficult to differentiate them to identify the root cause of on-orbit anomalies. This paper pioneers the comparative study of the “soft errors” induced by the SEE and SESD with a well-known static random-access memory (SRAM). The similarity and difference of the physical mechanisms between the “soft errors” induced by SEE and SESD are studied with the technology computer-aided design (TCAD) simulations. Meanwhile, the characteristics of the “soft errors” and the relation with external stimulus between SEE and SESD are further investigated with the pulsed laser SEE facility and SESD test system. The results showed that the similar appearances of “soft errors” can be generated by both SEE and SESD, while multiple-bit upset (MBU) has been observed only in SESD testing. In addition, in comparison to the random distribution of SEE sensitivity areas, the SESD sensitivity areas are in closer proximity to the power supply regions. The different symptoms in upsets can be used to identify the root causes of the spacecraft anomalies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".