Silicon photonics for the visible and near-infrared spectrum
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it