A New Transistor-Redundant Voter for Defect-Tolerant Digital Circuits
Bibliographic record
Abstract
As CMOS technology is being scaled down aggressively towards the nano-regime, digital circuits are becoming more and more prone to failure, not only because of transient faults, but more likely as a result of permanent defects. This paper presents a new technique for defect-tolerance at the transistor level called transistor redundancy (TR); targeting the voter design in fault-tolerant systems. This is the first time transistor redundancy is used to design the first defect-tolerant voter circuit. TR allows the masking of faults resulting from permanent defects, since it uses redundant transistors to implement the functionality of each transistor. Circuit simulations of n-bit TR-voter based triple modular redundancy (TMR) adder were conducted and the results were compared with conventional-voter based TMR adder. The use of the proposed TR-voter gives 100% fault masking capabilities (considering the single fault scenario) compared to fault-intolerant conventional-voter that does not mask any defect. There was no increase in the time delay but the total number of transistor, for each adder, increased by 25% compared to conventional TMR
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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 | 0.001 | 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".