Hemispherical retro-modulation technologies for passive free-space optical communication links
Bibliographic record
Abstract
In this work, we introduce the concept of a hemispherical retro-modulator for the realization of passive free-space optical communication links. The hemispherical retro-modulator is implemented with a high-refractive-index glass (S-LAH79) hemisphere on a semi-insulating-InP (SI-InP) layer, whose thickness dictates the effectiveness of both retroreflection and modulation. A voltage is applied across transparent indium tin oxide (ITO) and gold (Au) films on either side of the SIInP layer to bring about the desired modulation. The overall device is designed to enable low divergence on the retroreflected beam, as defined by a small divergence angle, and deep modulation on the retroreflected beam, as a result of electroabsorption in the SI-InP layer. To this end, the device is analysed with a ray-based model for retroflection and a unified Franz-Keldysh/Einstein model for modulation in the SI-InP layer. The theoretical results show strong agreement with the experimental results from our prototype. Moreover, the results show effective retroflection and deep modulation—with an applied electric field of 2.167 kV/cm yielding modulation depths of 13%, 34%, and 50% for our 980-nm photons and SI-InP layer thicknesses of 200, 600, and 1,000 μm, respectively. From this, we deem the SI-InP layer thickness of 600 μm to be optimal given its combined capabilities for retroflection and modulation. Ultimately, the introduced hemispherical retro-modulator is shown to be an effective element for future realizations of passive freespace optical communication links.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".