Fast detection of data retention faults and other SRAM cell open defects
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
Detection of open defects in static random access memory (SRAM) cells, including those causing data retention faults (DRFs), is known to be difficult and time consuming. This paper proposes a novel design-for-test (DFT) technique that allows SRAMs to be tested at full speed for these defects. As a result, it achieves not only significant test time reduction but also full coverage of open defects, including those undetectable to previous solutions. The proposed technique is referred to as predischarge write test mode (PDWTM). Implementation of the proposed technique requires little design effort and imposes negligible hardware and performance penalties. Furthermore, the proposed technique can be easily merged with any March algorithm, thus resulting in full DRF and other SRAM cell open defect coverage. The proposed technique has been validated by SPICE simulation using both low-power and high-speed SRAM cells.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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