SEU Performance of RHBD Flip-Flops Using Guard Gates at 22-nm FDSOI Technology Node
Bibliographic record
Abstract
Because of the isolation of transistors, fully depleted silicon-on-insulator (FDSOI) technology nodes have shown better single-event upset (SEU) resilience compared with bulk technology nodes. Additional radiation-hardening-by-design (RHBD) techniques can further improve the SEU performance. In this article, the SEU performance of multiple RHBD flip-flop (FF) designs using the guard-gate (GG) circuit at a 22-nm FDSOI technology is presented, including a conventional FF, a GG FF, a dual-feedback-recovery (DFR) FF, and a GG-dual-interconnected storage cell (DICE) FF. Irradiation results showed significant reductions in SEU cross sections for hardened designs compared to the conventional design. Specifically, the conventional GG design demonstrates more than$100\times $improvement over a conventional FF design, while DFR and GG-DICE designs showed no upsets for all test conditions. Further analysis was carried out to explain the SEU performance differences between the GG and DFR FF designs, and it is noted that proper layout arrangement is critical for achieving ideal SEU mitigation in this FDSOI 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 | 0.000 |
| 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".