On using twisted-ring counters for testing embedded cores in system-on-a-chip designs
Bibliographic record
Abstract
We present novel test set encoding and pattern decompression methods for core-based systems. These are based on the use of twisted-ring counters and offer a number of important advantages-significant test compression (over 10/spl times/ in many cases), less tester memory and reduced testing time, the ability to use a slow tester without compromising test quality or testing time, and no performance degradation for the core render test. Surprisingly, the encoded test sets obtained from partially-specified test sets (test cubes) are often smaller than the compacted test sets generated by automatic test pattern generation programs. Moreover, a large number of patterns are applied test-per-clock to cores, thereby increasing the likelihood of detecting non-modelled faults. Experimental results for the ISCAS benchmark circuits demonstrate that the proposed test architecture offers an attractive solution to the problem of achieving high test quality and low testing time with relatively slower, less expensive testers.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".