Bibliographic record
Abstract
Ferroelectric random-access memory (FeRAM) is an emerging nonvolatile memory technology that has several key advantages over flash memory, including much greater program-erase endurance and much faster write speed. However, FeRAM array storage capacities currently lag behind those of flash memory by more than three orders of magnitude; consequently, FeRAM has so far tended to be used only in niche applications, such as smart cards and electronic metering. Significant increases in FeRAM storage density requires progress on many technical fronts. Most digital memory technologies use two possible data signal levels to encode one bit per storage cell. Multilevel cell flash memory uses four data signal levels to increase the storage density to two bits per cell. In this paper we report the results of a preliminary study that investigated the possibility of using three data signal levels to increase the array storage density from 1 bit per cell to an average of 1.5 bits per cell. The principal challenge is to ensure the accurate writing of the three signal states (ferroelectric film polarized in the "up" and "down" directions, and a depolarized film) and the reliable sensing of cell states in the presence of noise and inevitable device parameter variations.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".