(Invited) Engineering Chalcogenide Materials – From Bulk Optics to CMOS-Compatible Microelectronic Integration
Bibliographic record
Abstract
Next generation optical and opto-electronic components will require materials that possess unique, spectrally agile, multi-functional attributes that can be produced via low(er) cost manufacturing processes. Material compositional design and novel processing and fabrication strategies are essential to the success in realizing new materials that fit application-specific needs. Efforts by our team have focused on use of IR transmissive glasses and crystalline alloys in planar form on Si, which lend themselves to integration with an on-chip source and semiconductor detector. Such devices exploit the enhanced sensitivity that comes from using probe light in the mid-infrared region (MIR) where applications such as sensors require materials which have spectral response that overlaps with fundamental molecular fingerprints of target analytes. We report progress on designing CMOS-compatible materials and processes aimed at adding to the ‘photonic material toolbox’ which exploit other functions including phase change or high mobility materials for emerging electronics applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".