Impact of technology scaling on bridging fault detections in sequential and combinational CMOS circuits
Bibliographic record
Abstract
It is well known that classical fault models (stuck-at, stuck-open, stuck-on) cover only partially the spectrum of failures in today's integrated circuits (IC). Some realistic failures occurring in logic circuits have to be considered at the physical level and include its electrical behavior. Among these failures, gate-oxide short, floating gate and bridging fault types may produce intermediate voltages with difficult interpretations at logic level. This work investigates the influence of a bridging fault (BF) between two interconnection lines on the logic margin and logic swing of an IC and the sensitivity of digital ICs realized on four different technologies (0.25 /spl mu/m, 0.35 /spl mu/m, 0.5 pm. 1.5 /spl mu/m) to bridging faults. Several circuits, including D flip-flops and ISCAS benchmark circuits, were analyzed to find out the impact of technology scaling on BF defects detection. In this work we show that the sensitivity of an IC to BF is increased with technology scaling. The testing methodology was based on the use of voltage, temperature and frequency as parameters, which influence on the behavior of an IC with BF.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".