Hybrid test vector compression in system-on-chip test — An overview and methodology
Bibliographic record
Abstract
This paper presents a comprehensive study on a number of hybrid test vector compression methods for VLSI circuit testing. In the proposed approaches, a software program is loaded into the on-chip processor memory along with the compressed test data sets. To minimize on-chip storage besides testing time, the test data volume is first reduced by compaction in a hybrid manner before downloading into the processor. The methods utilize a set of adaptive coding techniques for realizing lossless compression. The compaction program need not be loaded into the embedded processor, as only the decompression of test data is required for the automatic test equipment. The developed schemes necessitate minimal hardware overhead, while the on-chip embedded processor can be reused for normal operation on completion of testing. As an extension of the earlier works, this paper also reports further results on studies of the problem and demonstrates the feasibility of the suggested methodologies with simulation results on ISCAS 85 combinational and ISCAS 89 full scan sequential benchmark circuits.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".