Comprehensive non-functional analysis of combinational circuits vulnerability to single event transients
Bibliographic record
Abstract
The progressive shrinking of device sizes in advanced technologies leads to miniaturization and performance improvements. However, ultra-deep sub-micron technologies are more vulnerable to different types of uncertainties, parametric variations, and interference. In this paper, we propose a methodology to model and analyze the behavior of a system in the presence of Single Event Transients (SETs). The problem of SET propagation was modeled as a satisfiability problem using different satisfiability modulo theories. The SET width and timing constraints are formulated as a difference logic constraint satisfaction formulation. This formulation utilizes concepts from static timing analysis to efficiently evaluate the required time and width for the SET to be latched. Next, the proposed model is analyzed using efficient SMT solvers for a set of nonfunctional assertions to investigate SETs propagation. Based on the results of this analysis, new fault observability estimates are computed. These values are then used to compute the soft error rate. Experimental results demonstrate that the proposed SMT approach provides better runtime then contemporary techniques.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".