Determination of the optimum cluster parameters for a clustered free-space optical interconnect
Bibliographic record
Abstract
Parallel free-space parallel optical interconnects (FSOIs) have great potential for use as high bandwidth interconnects at the board-to-board and chip-to-chip levels Recent reports of the integration of large (1024) arrays of vertical-cavity surface-emitting laser (VCSEL) arrays to complementary metal-oxide semiconductor (CMOS) suggest that large parallel interconnects should be possible in this technology. We have introduced a technique to determine the optimum cluster dimensions for a free-space optical interconnect which deliver the maximum channel density for a given degree of misalignment tolerance. The sources are assumed to be multimode VCSELs (wavelength 850 nm, mode field diameter 6 /spl mu/m, M2 factor of 1.93 and VCSEL pitch 125 /spl mu/m). They are collimated by microlenses with a focal length of 250 /spl mu/m. The required interconnection distance results in a minilens focal length of 8.5 mm (assuming a single relay block to route the optical channels). This technique will be extended to cover arbitrary focal lengths and source parameters by incorporating an analytical calculation of ray aberrations. This approach has the potential to considerably simplify the design of clustered free-space optical interconnects.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".