Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Compared with traditional functional fault models, fault model obtained from an Inductive Fault Analysis (IFA) test flow can provide an attractive basis for obtaining a good estimate of the overall test quality in terms of defect level and yield. However, the associated surging test time due to increased SRAM capacity is becoming a major challenge when testing either standalone or embedded SRAMs. This paper refines the functional fault models translated from defect simulations for embedded SRAMs with IFA proposed and described. Reconsidering the defect causes of the functional faults allows us to simplify the functional fault model FFM2 and formulate the test time required for detecting Data Retention Faults. We combine this simplification with the consideration of specific memory redundancy elements to develop a new March 6N Test algorithm. Simulation results reveal that our proposed fault modeling and test generation algorithm can reduce total test time to one half or less of that required by the methodology, while maintaining the same defect and fault coverage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it