Efficacy of Transistor Stacking on Flip-Flop SEU Performance at 22-nm FDSOI Node
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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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$17\times $ </tex-math></inline-formula> 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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 it