First-Band S-Vector Photonic-Crystal Superprism Demultiplexer Design and Optimization
Bibliographic record
Abstract
In this paper, we present a complete approach to the design of a wavelength demultiplexer based on the S-vector superprism photonic-crystal phenomenon. We make use of a full 3-D modeling approach based on plane-wave-expansion method, which allows the full dynamics of beam propagation to be considered. This reveals significant nonuniformities in beam divergence and dispersion as a function of wavelength, which has been neglected in previous 2-D models and which reduces the scalability of these devices. We examine 1-D and 2-D photonic-crystal lattices and show that the 1-D lattice results in the smallest superprism area as a function of channel count. This is due to its lower band curvature relative to 2-D square and hexagonal lattices, even though it has much lower angular dispersion. We also modify the previous S-vector superprism design so that, for each channel, the prism region extends only as far as necessary for channel resolution at a specified crosstalk level. Based on silicon-on-insulator (SOI) technology, with a top silicon layer of 260 nm and minimum feature size of 75 nm, we present the design of a four-channel coarse-wavelength-division-multiplexing demultiplexer with theoretical crosstalk of 20 dB, which has a superprism area of 1367 mum <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".