On-chip test generation scheme based on reconfigurable programmable and multiple twisted-ring counters
Bibliographic record
Abstract
Built-in-self-test (BIST) has emerged as a promising solution to VLS I testing problems. The test pattern generation scheme using twisted-ring-counters is more efficient than the pseudo random testing method in detecting random-pattern-resistant faults. Related work based on single fixed-order twisted-ring-counter design requires long test time to achieve high fault coverage and large storage space to store the seeds and the control data. By using multiple programmable twisted-ring-counters (PTRC), a significant reduction in test application cycles were achieved. In this paper, a reconfigurable programmable multiple twisted-ring-counter is proposed to minimize the test time and to generate more number of different test patterns. Here the programmable twisted-ring-counter operates depending on the control signal of the block select module, thus we can generate more number of patterns with less time. The design was modeled in VHDL and simulated using Modelsim SE 6.2 b simulator. Synthesis was done using Xilinx IS E 14.2.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".