Efficacy of Transistor Interleaving in DICE Flip-Flops at a 22 nm FD SOI Technology Node
Bibliographic record
Abstract
Fully Depleted Silicon on Insulator (FD SOI) technology nodes provide better resistance to single event upsets than comparable bulk technologies, but upsets are still likely to occur at nano-scale feature sizes, and additional hardening techniques should be explored. Three flip-flop designs were implemented using Dual Interlocked Cell (DICE) latches in a 22 m FD SOI technology node. Additional hardening was implemented in the layout of each design by using transistor spacing and interleaving. Comparisons were made between a standard DICE design and two other designs making use of the new Continuous Active (CnRx) Diffusion construct and guard-gate transistor stacking through alpha particle and heavy ion irradiation. Designs making use of the CnRx construct for performance improvements were more likely to experience upsets due to higher collected charges in the increased diffusion regions. Conversely, transistor stacking showed strong soft error rate resilience because of the natural isolation between transistors in the FD SOI technology. Overall, the efficacy of transistor interleaving in flip-flops using DICE latches was found to be extremely robust in the 22 nm FD SOI technology node.
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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 | 0.001 |
| 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".