Holistic Validation Pattern Generation for IEEE 1687 and Streaming Scan Networks
Bibliographic record
Abstract
The increasing complexity of Integrated Circuits (ICs) is driven by heterogeneous functionality and stringent performance demands. This necessitates scalable and efficient design for testability (DFT) solutions to ensure cost-effective test access and functional correctness. Streaming Scan Network (SSN) and High-Bandwidth IJTAG over SSN (HB-IJTAG) enhance the test efficiency significantly by accelerating the data transfer and optimizing the test execution. However, these technologies introduce validation challenges due to more intricate control mechanisms and their large-scale deployment.This paper presents a novel, holistic approach for generating and sequencing functional validation patterns. These patterns systematically leverage SSN and HB-IJTAG capabilities to optimize overall efficiency. The proposed methodology enables the concurrent and robust validation of hundreds of SSN and HB-IJTAG DFT components, significantly improving the overall test execution time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".