Development of silicon immersed grating for METIS on E-ELT
Bibliographic record
Abstract
We have developed the technology to manufacture an immersed grating in silicon for the Mid-infrared E-ELT Imager and Spectrograph, METIS. We show that we can meet the required diffraction-limited performance at a resolution of 100000 for the L and M spectral bands. Compared to a conventional grating, the immersed grating drastically reduces the beam diameter and thereby the size of the spectrometer optics. As diffraction takes place inside the high-index medium, the optical path difference and angular dispersion are boosted proportionally, thereby allowing a smaller grating area and a smaller spectrometer size. The METIS immersed grating is produced on a 150 mm industry standard for wafers and replaces a classical 400 mm echelle. Our approach provides both a feasible path for the production of a grating with high efficiency and low stray light and improves the feasibility of the surrounding spectrometer optics. In this contribution we describe and compare the classical-grating solution for the spectrometer with our novel immersed-grating based design. Furthermore, we discuss the production route for the immersed grating that is based on our long-standing experience for space-based immersed gratings. We use standard techniques from the semiconductor industry to define grating grooves with nanometer accuracy and sub-nanometer roughness. We then use optical manufacturing techniques to combine the wafer and a prism into the final immersed grating. Results of development of the critical technology steps will be discussed.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".