Fault modeling and pattern-sensitivity testing for a multilevel DRAM
Bibliographic record
Abstract
Multilevel dynamic random-access memory (MLDRAM) attempts to increase the storage density of semiconductor memory without further reducing the lithographic dimensions. It does so by using more than two possible signal voltages on each cell capacitor thus permitting more than one bit to be stored in each cell. Birk's MLDRAM scheme has several promising properties, including robust locally-generated data signal and reference signal generation, and fast flash-conversion sensing. This paper describes a fault model for Birk's MLDRAM that was developed by considering the behaviors produced by likely defects at the schematic level. The resulting behaviors include faults that are detectable as observable logical errors, faults that can be detected by current measurements, and faults that, in the worst case, can only be detected by testing for degraded noise margins. All Boolean faults in the fault model can be detected by an efficient test whose length grows linearly in the number of cells. The narrower noise margins in MLDRAM will make it more vulnerable to pattern sensitivities. We also developed a linear test that evaluates worst-case sensing conditions.
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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 | 0.001 |
| 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) | 0.000 | 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".