Active metasurface designs for lensless and detector‐limited imaging
Bibliographic record
Abstract
Abstract The emergence of metasurfaces has enabled lightweight, compact imaging with degrees of freedom which previously required complex optical setups to achieve, such as polarization, wave vector, and spectrum. To date, most metasurface‐enabled imaging systems have thus far been ‘passive’, and therefore subject to fundamental information and thickness limits set by the coupling of light to their sensor arrays. We discuss the use of active metasurfaces in low form‐factor and low pixel‐count imaging systems and introduce a prototypical lensless imaging system concept which employs an active metasurface as a high‐frequency, continuously tunable amplitude and phase modulation aperture, coupled to a discrete single‐pixel detector. We analyze the scalability of such a platform and computationally demonstrate that a scalable ‘perimeter‐control’ addressing architecture – in which a M × N rectangular array of scattering elements is addressed by only M + N voltages – is sufficient for image collection, even when scatterers exhibit limited phase control, and undesired amplitude variations. We also address fundamental limits in information collection, image aberrations, and signal‐to‐noise ratio, highlighting key advantages, limitations, and trade‐offs for active metasurface imaging. We generalize our discussion to other active metasurface‐enabled imaging configurations and applications. Finally, we consider promising active metasurface material platforms with an outlook towards new directions to enable high‐efficiency imaging.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".