Synthesized transparent BIST for detecting scrambled pattern-sensitive faults in RAMs
Why this work is in the frame
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Bibliographic record
Abstract
This paper describes a synthesizable, transparent, built-in self-test (BIST) scheme for random-access memories (RAMs). By altering only two parameters in a VHDL specification, BIST circuits can be automatically generated to detect 2-, 3- or 4-cell write-triggered coupling faults as well as two different classes of 5-cell faults. The 5-cell faults represent either unlinked scrambled active physical neighborhood pattern-sensitive faults (PNPSFs), or arbitrary combinations of unlinked scrambled active, static, and passive PNPSFs. The BIST scheme uses a modified version of Nicolaidis' method to make the applied tests transparent; thus the data that were held in the RAM at the start of the test will be restored by the end of the test, if no faults are present. All single faults of the above fault types, as well as most other standard fault types, are guaranteed to be detected because of the use of an aliasing-free signature analyzer. By comparing numerous intermediate signatures, the new design has a very low probability of aliasing when multiple faults are present.
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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