Multiplexed computer-generated hologram with polygonal apertures
Bibliographic record
Abstract
A novel type of multiplexed computer-generated hologram (CGH) is designed with more than one billion of pixels per period. It consists of elementary cells divided into arbitrary-shaped polygonal apertures, the division being identical in all cells. The cells are further digitized into pixel arrays to exploit the huge space-bandwidth product of electron-beam lithography. The polygonal apertures in the same location inside the cells constitute a subhologram. With the Abbe transform that has never, to our knowledge, been used in other CGH designs, the subhologram images (subimages) are obtained with fast Fourier transforms. It is therefore possible to design a multiplexed CGH that has a size thousands of times larger than the manageable size of a conventional CGH designed with the iterative Fourier transform algorithm (IFTA). A much larger object window than that of the conventional CGH can also be achieved with the multiplexed polygonal-aperture CGH, owing to its extremely large dimensions. The multiplexed polygonal-aperture CGH is designed with the novel iterative subhologram design algorithm, which considers the coherent summation of the subimages and applies constraints on the total image, subimages, and subholograms. As a result, the noise appearing in the preceding multiplexed-CGH designs is avoided. The multiplexed polygonal-aperture CGH has a much higher diffraction efficiency than that resulting from either the preceding multiplexed-CGH designs or the conventional CGH designed by the IFTA.
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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) | 0.001 | 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".