Efficacy of Transistor Stacking on Flip-Flop SEU Performance at 22-nm FDSOI Node
Bibliographic record
Abstract
Fully-depleted silicon-on-insulator (FDSOI) technology nodes offer better single-event (SE) performance compared with comparable bulk technologies. However, upsets are still possible at nanoscale feature sizes and additional hardening techniques need to be explored. This article presents the single-event upset (SEU) performance of multiple flip-flop (FF) designs using the stacked-transistor hardening technique at a 22-nm FDSOI technology node. Irradiation results show significant reductions in SEU cross sections for stacked-transistor-based hardened designs compared to a conventional design. Alpha particle exposures showed zero upsets for all D-flip-flop (DFF) designs tested. When exposed to heavy-ions, the stacked-transistor DFF design showed a$17\times $improvement over a conventional DFF design at an LET value of 47 MeV-cm2/mg. The stacked-transistor design with the charge-canceling technique showed upsets when particle LET exceeded 93.8 MeV-cm2/mg and at a high angle of incidence. The stacked-transistor design with the interleaving technique showed zero upsets for all test conditions.
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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) | 0.002 | 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".