Spectral retrieval techniques for high-resolution Fourier transform microspectrometers
Bibliographic record
Abstract
Spatial heterodyne Fourier transform (SHFT) spectroscopy is based on simultaneous interferometric measurements implementing linearly increasing optical path differences, hence circumventing the need for mechanical components of traditional Fourier transform spectroscopy schemes. By taking advantage of the high mode confinement of the Siliconon-Insulator (SOI). platform, great interferometric lengths can be implemented in a reduced footprint, hence increasing the resolution of the device. However, as resolution increases, spectrometers become progressively more sensitive to environmental conditions, and new spectral retrieval techniques are required. In this work, we present several software techniques that enhance the operation of high-resolution SHFT micro-spectrometers. Firstly, we present two techniques for mitigating and correcting the effects of temperature drifts, based on a temperature-sensitive calibration and phase errors correction. Both techniques are demonstrated experimentally on a 32 Mach-Zehnder interferometers array fabricated in a Silicon-on-insulator chip with microphotonic spirals of linearly increasing length up to 3.779 cm. This configuration provides a resolution of 17 pm in a compact device footprint of 12 mm2. Secondly, we propose the application of compressive-sensing (CS) techniques to SHFT micro-spectrometers. By assuming spectrum sparsity, an undersampled discrete Fourier interferogram is inverted using l1-norm minimization to retrieve the input spectrum. We demonstrate this principle on a subwavelength-engineered SHFT with 32 MZIs and a 50 pm resolution. Correct retrieval of three sparse input signals was experimentally demonstrated using data from 14 or fewer MZIs and applying common CS reconstruction techniques to this data.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".