Performance of silicon immersed gratings: measurement, analysis, and modeling
Bibliographic record
Abstract
The use of Immersed Gratings offers advantages for both space- and ground-based spectrographs. 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. Short-wave infrared (SWIR) spectroscopy is used in space-based monitoring of greenhouse and pollution gases in the Earth atmosphere. On the extremely large telescopes currently under development, mid-infrared high-resolution spectrographs will, among other things, be used to characterize exo-planet atmospheres. At infrared wavelengths, Silicon is transparent. This means that production methods used in the semiconductor industry can be applied to the fabrication of immersed gratings. Using such methods, we have designed and built immersed gratings for both space- and ground-based instruments, examples being the TROPOMI instrument for the European Space Agency Sentinel-5 precursor mission, Sentinel-5 (ESA) and the METIS (Mid-infrared E-ELT Imager and Spectrograph) instrument for the European Extremely Large Telescope. Three key parameters govern the performance of such gratings: The efficiency, the level of scattered light and the wavefront error induced. In this paper we describe how we can optimize these parameters during the design and manufacturing phase. We focus on the tools and methods used to measure the actual performance realized and present the results. In this paper, the bread-board model (BBM) immersed grating developed for the SWIR-1 channel of Sentinel-5 is used to illustrate this process. Stringent requirements were specified for this grating for the three performance criteria. We will show that –with some margin– the performance requirements have all been met.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".