Silicon photonics for the visible and near-infrared spectrum
Bibliographic record
Abstract
Emerging applications in quantum information, microscopy, biosensing, depth sensing, and augmented reality demand miniaturized components in the visible (VIS) and near-infrared (NIR) spectrum with wavelengths between 380 and 1100 nm. Foundry silicon photonics, which has been optimized for telecommunication wavelengths, can be adapted to this wavelength range. In this article, we review recent developments in silicon photonics for VIS and NIR wavelengths, with a focus on platforms, devices, and photonic circuits fabricated in foundries. Foundries enable the creation of complex circuitry at a wafer scale. Platforms based on silicon nitride and aluminum oxide wave-guides compatible with complementary metal–oxide–semiconductor (CMOS) foundries are becoming available. As a result, highly functional photonic circuits are becoming possible. The key challenges are low-loss waveguides, efficient input/output coupling, sensitive detectors, and heterogeneous integration of lasers and modulators, particularly those using lithium niobate and other electro-optic materials. These elements, already developed for telecommunications, require further development for λ < 1100 nm. As short-wavelength silicon photonics technology advances, photonic integrated circuits can address a broader scope of applications beyond O- and C-band communication.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".