Power, Delay and Yield Analysis of BIST/BISR PLAs Using Column Redundancy
Bibliographic record
Abstract
As the number of transistors on a chip begins to exceed 1 billion, it is mandatory to use 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. However, this requires the use of structured logic, such as programmable logic arrays (PLAs) or structured ASIC. In this paper, we select the fastest and lowest energy PLA design to date and combine it with a block duplication strategy to construct a BIST/BISR PLA in order to establish a reference design. Then, we introduce a PLA redundancy in the form of spare columns and carry out a yield analysis. The results of the yield analysis suggest that using duplication for BIST/BISR is better suited for small PLAs while using spares is more suitable for larger PLAs. The spares needed are determined by several factors including target yield, area, power and delay
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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 | 0.002 |
| 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 | 0.000 |
| 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".