Design of a multilevel DRAM with adjustable cell capacity
Bibliographic record
Abstract
A multilevel DRAM (MLDRAM) increases the per-cell storage capacity over conventional DRAM by using more than two cell signal levels. The key challenge when designing an MLDRAM is to ensure reliable operation using the more closely spaced signal levels despite the presence of on-chip noise and the inevitable small variations in circuit parameters that occur in integrated circuit (IC) production. This paper describes a test chip that implements an inherently balanced and robust MLDRAM scheme proposed by Birk et al. (see 1999 IEEE Int. Workshop on Memory Tech., Design and Testing, San Jose, CA, USA, p.102-109.). The chip has an adjustable cell capacity that can be selected from among 1, 1.5, 2 and 2.5 bits per cell. Fractional bits arise when groups of two or more cells are considered together. Thus if each cell in a pair stores one of six possible levels, then each cell has a capacity of 2.5 bits. The test chip should facilitate the experimental characterization of the proposed MLDRAM scheme.
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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 | 0.000 |
| 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 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".