Bibliographic record
Abstract
The trend in semiconductor manufacturing over the last decade has been an accelerated rate for both materials integration and wafer fabrication process development. While the cutting edge of semiconductor technology is driven by digital logic and memory applications, several other technology sectors benefit from innovation by the leaders. Of these technology sectors, image sensor manufacturers have realized many benefits from the selective use of developments within advanced technology node manufacturing. The motivations for the imaging industry to pursue Moore's Law type of scaling are comparable to that of the broader semiconductor industry. Additionally, image sensor companies seek a reduction of camera module form factor, an increase in pixel resolution, and an increase in pixel array performance. Today, semi-professional grade digital single-lens reflex (DSLR) pixels have scaled down to the size of what were state-of-the-art “small pixel” consumer grade camera phone sensors just a few years ago. The pixel size of recent camera phones has shrunk to 1.12 μm. The resolution for recent camera phones has reached 16.4 Mp. Beyond silicon foundry processes, imaging companies must also concern themselves with the optical systems and packaging solutions required to integrate their silicon devices with the consumer electronics supply chain. Chipworks, as a supplier of competitive intelligence to the semiconductor and electronics industries, monitors the evolution of image sensor technologies as they come into production. Chipworks has obtained charge-coupled devices (CCD) and CMOS image sensor (CIS) chips from leading manufacturers and performed structural, compositional, and design analyses to benchmark the technology of the market leaders.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".