Functional Constraint Extraction From Register Transfer Level for ATPG
Bibliographic record
Abstract
The use of scan test patterns, generated at the gate level with automatic test pattern generation (ATPG) tools in design simulation, was proposed in our previous work to improve verification quality. A drawback of this method is the potential presence of illegal (or unreachable) states (ISEs) causing unwanted behavior and false error detection in the verification process. In this brief, we present a new automated tool that helps overcome this problem. The tool extracts functional constraints at the register transfer level on a VHDL description (it can be easily adapted to any other hardware description language). The constraints extracted are used in the ATPG process to generate pseudofunctional scan test patterns which avoid the ISEs. The whole verification environment incorporating the proposed tool is presented. Experimental results show the tool impact on the reduction of false error detection in verification. In addition, it shows the verification quality improvements with the proposed environment in terms of coverage, time, and complexity.
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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 | 0.000 | 0.001 |
| 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".