Single-Event Transient Study of 28 nm UTBB-FDSOI Technology Using Pulsed Laser Mapping
Bibliographic record
Abstract
Single-event transient (SET)-induced soft errors are becoming a more significant threat to the reliability of electronic systems in space, especially for advanced technologies. The SET pulse width, which is vulnerable to SET propagation, is a critical parameter for developing SET mitigation techniques. This paper investigates the pulse-broadening effect in the process of SET propagation in logic circuits and the SET-sensitive region distribution in the layout using the pulsed-laser mapping technique in logic circuits implemented with 28 nm Ultra-Thin Body and BOX (UTBB) FDSOI technology. The experiments were carried out at the Naval Research Laboratory (NRL) to measure the SET-induced errors and map the SET-sensitive region distribution at various clock frequencies and laser energy levels. The results illustrate that the number of errors increases with the clock frequency and energy for combinational logic circuits and that the flip-flop SEU rate is less sensitive to clock frequency. The SET pulse-broadening effect was also observed using SET mapping for an OR gate chain at different laser energy levels. In addition, the simulation results revealed the mechanism of the SET pulse-broadening effect in an OR gate chain.
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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 | 0.000 |
| 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) | 0.001 | 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".