New Analog Test Metrics Based on Probabilistic and Deterministic Combination Approaches
Bibliographic record
Abstract
The continuous characteristic of the parametric faults spectrum, the process variations and their masking effects are major difficulties limiting the development of efficient test generation for parametric faults. Moreover, there is a need for accurate test metrics to quantify the quality of a test set and to determine whether the testability is adequate. An analog test metric called parameter fault coverage (PFC) was recently introduced by the authors. The PFC metric takes into account the combination of the above major difficulties. In this paper, we consider parametric faults caused by the increased variance in device parameters. We introduce two novel metrics: one is called guaranteed parameter fault coverage (GPFC), which is the guaranteed lower bound of the PFC, and the other one is called partial parameter fault coverage (PPFC), which is the probabilistic component of the PFC. We combine the deterministic metric GPFC and the probabilistic metric PPFC to produce a PFC metric that enables accurately measuring the analog test quality and allows precisely measuring testability, thus avoiding the drawbacks of incorrect decisions regarding the use of design for testability (DFT) techniques. Also, we show that when DFT is used to improve circuit testability, PFC becomes dominated by the deterministic component GPFC, while the probabilistic component PPFC is minimized. This paper demonstrates the effectiveness of our approach on an illustrative example.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".