Electronically Controlled Semiconductor Nanoparticle Array for Tunable Plasmonic Metasurfaces
Bibliographic record
Abstract
Plasmonic Metasurfaces (PMs) offer unprecedented ways to manipulate optical wavefronts with an ultra-thin layer of materials. Until recently, the research efforts have focused on designing passive metasurfaces. However, gaining high-speed, reversible control over individual pixels (basic building block) in these engineered structures can offer better and faster ways to control and shape light. Conventionally used tuning approaches target the whole substrate by either utilizing mechanically moving frames or tuning the refractive index of the whole substrate. Conceptualizing a high-speed, switching mechanism for locally tuning pixel/meta-atom will allow new applications that were previously unimaginable. Here we introduce a novel approach for tunable plasmonic meta-atoms via modulation doping in semiconductor nanostructures at the telecommunication window which can potentially be used for local control in PMs. The proposed approach is based on (voltage-controlled) tuning the quantum confinement of the charge carrier from 1-D to 0-D in semiconductor nanorods. The applied field allows accumulation of excess charge carrier density and facilitates tuning plasmonic resonance of nanoresonators from 1800 – 1550 nm. A high-speed voltage-controlled localized surface plasmon resonance is reported in semiconductor nanostructures fabricated via a cost-effective, scalable, self-assembly process based on aluminum anodization. Moreover, the concept in-principle will be compatible with most semiconductors allowing exciting applications in tunable metasurfaces, spasers, modulators, and many more.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".