SRAM Retention Testing: Zero Incremental Time Integration with March Algorithms
Bibliographic record
Abstract
Testing data retention faults (DRFs), particularly in integrated systems on chip comprised of very large number of various sizes and types of embedded SRAMs is challenging and typically time-consuming due to the required pause time that needs to be introduced in the test session. This paper proposes a novel technique, referred to as pre-discharge write test mode (PDWTM), that effectively integrates the testing of DRF within "regular" March algorithms such that the rate (speed) of the latter remains unaltered. That is, the PDWTM enables DRF testing without incurring the additional cycles or pauses in the March test execution thereby enabling additional coverage at no expense in terms of overall test time. We show that DRFs can be easily detected by pre-discharging bit lines before a write operation. Here, the PDWTM is evaluated using both high-speed and low power memory cells, representing two extreme cases based on the typical memory design methodologies.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".