Addressing useless test data in core-based system-on-a-chip test
Bibliographic record
Abstract
This paper analyzes the test memory requirements for core-based systems-on-a-chips and identifies useless test data as one of the contributors to the total amount of test data. The useless test data comprises the padding bits necessary to compensate for the difference between the lengths of different chains in multiple scan chain designs. Although useless test data does not represent any relevant test information, it is often unavoidable, and leads to the tradeoff between the test bus width and the volume of test data in multiple scan chain-based cores. Ultimately, this tradeoff influences the test access mechanism design algorithms leading to solutions that have either short test time or low volume of test data. Therefore, in this paper, a novel test methodology is proposed which, by dividing the wrapper scan chains (WSCs) into two or more partitions, and by exploiting automated test equipment memory management features, reduces the amount of useless test data. Extensive experimental results using ISCAS'89 and ITC'02 benchmark circuits are provided to analyze the implications of the number of WSCs in the partition, and the number of partitions on the proposed methodology.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".