Scan Architecture With Mutually Exclusive Scan Segment Activation for Shift- and Capture-Power Reduction
Bibliographic record
Abstract
Power dissipation during scan testing is becoming an important concern as design sizes and gate densities increase. While several approaches have been recently proposed for reducing power dissipation during the shift cycle (minimum transition don't care fill, special scan cells and scan chain partitioning), very little work has been carried out towards reducing the peak power during test response capture and the few existing approaches for reducing capture power rely on complex ATPG algorithms. This paper proposes a scan architecture with mutually exclusive scan segment activation which overcomes the shortcomings of previous approaches. The proposed architecture achieves both shift and capture power reduction with no impact on the performance of the design, and with minimal impact on area and testing time (typically 2-3%). An algorithmic procedure for assigning flip-flips to scan segments enables reuse of test patterns generated by standard ATPG tools. An implementation of the proposed method had been integrated into an automated design flow using commercial synthesis and simulation tools which was used on a wide range of benchmark designs. Reductions up to 57% in average power, and up to 44% and 34% in peak power dissipation during shift and capture cycles, respectively, were obtained when using two scan segments. Increasing the number of scan segments to six leads to reductions of 96% and 80% in average power and respectively maximum number of simultaneous transitions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.003 | 0.001 |
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".