Partitioning for Selective Flip-Flop Redundancy in Sequential Circuits
Bibliographic record
Abstract
As the number of transistors on a chip begins to exceed 1 billion and their sensitivity to defects begins to degrade overall yield, it will be mandatory to assign a portion of the transistors for the purposes of built-in- self-test (BIST) and built-in-self-repair (BISR) as part of the supporting circuitry. Here, we focus on the self-test and self-repair of flip-flops (FF's), and their associated interconnect, using spare FF's to replace faulty ones. We describe our method to determine the number of spares based on delay and yield analysis. Using these results, we partition the flip-flops in a sequential design to improve the yield while keeping the delay and area overhead low. Next, we apply this redundancy approach only to non-critical paths in the circuit so that no timing penalty is incurred, and find that it can still provide significant improvement in the overall yield. A number of sequential benchmark circuits from ITC '99 are compared with and without redundant flip-flops, and also with and without partitioning. The total area overhead of our method is 8% on average while improving the yield by 6-29% and incurring no timing penalty.
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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 | 0.001 | 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".