Hyperspectral Imager with Folded Metasurface Optics
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Hyperspectral imaging is a key characterization technique used in various areas of science and technology. Almost all implementations of hyperspectral imagers rely on bulky optics including spectral filters and moving or tunable elements. Here, we propose and demonstrate a line-scanning folded metasurface hyperspectral imager (HSI) that is fabricated in a single lithographic step on a 1 mm thick glass substrate. The HSI is composed of four metasurfaces, three reflective and one transmissive, that are designed to collectively disperse and focus light of different wavelengths and incident angles on a focal plane parallel to the glass substrate. With a total volume of 8.5 mm 3, the HSI has spectral and angular resolutions of ∼1.5 nm and 0.075°, over the 750–850 nm and −15° to +15° degree ranges, respectively. Being compact, light weight, and easy to fabricate and integrate with image sensors and electronics, the metasurface HSI opens up new opportunities for utilizing hyperspectral imaging where strict volume and weight constraints exist. In addition, the demonstrated HSI exemplifies the utilization of metasurfaces as high-performance diffractive optical elements for implementation of advanced optical systems.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".