Synthesized transparent BIST for detecting scrambled pattern-sensitive faults in RAMs
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.
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.
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 | 0.000 |
| Open science | 0.000 | 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".