Integrating Sphere Fourier Microscopy of Highly Directional Emission
Bibliographic record
Abstract
Accurately controlling light emission using nano- and microstructured lenses and antennas is an active field of research. Dielectrics are especially attractive lens materials due to their low optical losses over a broad bandwidth. In this work we measure highly directional light emission from patterned quantum dots (QDs) aligned underneath all-dielectric nanostructured microlenses. The lenses are designed with an evolutionary algorithm and have a theoretical directivity of 160. The fabricated structures demonstrate an experimental full directivity of 61 ± 3, three times higher than what has been estimated before, with a beaming half-angle of 2.6°. This high value compared to previous works is achieved via three mechanisms. First, direct electron beam patterning of QD emitters and alignment markers allowed for more localized emission and better emitter-lens alignment. Second, the lens fabrication was refined to minimize distortions between the designed shape and the final structure. Finally, a new measurement technique was developed that combines integrating sphere microscopy with Fourier microscopy. This enables complete directivity measurements, contrary to other reported values, which are typically only partial directivities or estimates of the full directivity that rely partly on simulations. The experimentally measured values of the complete directivity were higher than predicted by combining simulations with partial directivity measurements. High directivity was obtained from three different materials (cadmium-selenide-based QDs and two lead halide perovskite materials), emitting at 520, 620, and 700 nm, by scaling the lens size according to the emission wavelength.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".