Building a Metrology Exchange to Innovate in Semiconductors (METIS)
Bibliographic record
Abstract
There is an immediate need to make NIST research funded through the CHIPS Metrology Program available in a manner that guards intellectual property, protects U.S. security interests, is aligned with the approach used by NIST for access to research results, and is self-sustaining to meet future needs. Establishing a data exchange ecosystem will meet that need, giving stakeholders access to CHIPS Metrology research results and serving to catalyze breakthroughs in U.S. semiconductor manufacturing. We call this data exchange concept “METIS”—Metrology Exchange to Innovate in Semiconductors—after Metis, the Greek goddess of innovative ideas, good counsel, skill, and craft. The METIS concept leverages and builds upon currently existing data management systems and processes at NIST, an organization uniquely qualified to both produce and manage leading metrology research and technical data products. By designing the data ecosystem around and for research needed by the public, industry, and the scientific community, METIS serves as a virtual platform for curation of microelectronics research data and tools with security and controls to enable the final products to reach their intended recipients.
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 | 0.024 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".