Bibliographic record
Abstract
Recent generations of Dynamic Random Access Memory (DRAM) have seen remarkable changes in both processes and the materials used. In the past five years the industry has gone from the 9x-nm node through the 7x, 6x, and 5x nodes to the 4x node chips starting to come on the market. In retrospect it is little short of amazing that the competing companies have crammed the required ~25 fF into an ever decreasing amount of floorspace; the cell size of the latest 4x-nm DRAM is ~0.0137 μm2- or maybe we should say ~13,700 nm2. This has been achieved by the adoption of dual-layer capacitors, high-k dielectrics, and raised source/drains, among other techniques. Chipworks, as a supplier of competitive intelligence to the semiconductor and electronics industries, monitors the evolution of chip technologies as they come into commercial production. Chipworks has obtained parts from the leading edge manufacturers, and performed structural and compositional analyses to examine the features and manufacturing processes of the devices. This paper illustrates some of the different structures of DRAM cells seen in the last few years from some of the leading companies in the sector, ranging from the 9x-nm node to the latest 4x-nm product.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.011 |
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".