Open defects detection within 6T SRAM cells using a No Write Recovery Test Mode
Bibliographic record
Abstract
The detection of all open defects within 6T SRAM cells is always a challenge due to the significant test time requirements. This paper proposes a new design-for-test (DFT) technique that we refer to as No Write Recovery Test Mode (NWRTM) to detect all open defects, some of which produce Data Retention Faults (DRFs) but are undetectable by typical March tests. We demonstrate the effectiveness of our proposed technique by only applying it to fault-free memory cells and faulty cells with those undetectable defects but all the open defects are covered since our DFT technique is implemented by simply adding extra test cycles into typical March tests. Two 6T SRAM cell models, one a high-speed version and the other a low-power one, representing extreme cases according to traditional design methodologies, were designed to validate our proposed NWRTM at the circuit level. Simulation results show that our NWRTM amounts to a shorter total test time and improved open defect detection capability. In addition, in comparison to other DFT techniques, NWRTM requires the least additional design effort, and imply less area and no performance penalties.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".