Surface Susceptibility Synthesis of Metasurface Holograms for Creating Electromagnetic Illusions
Bibliographic record
Abstract
A systematic approach is presented to exploit the rich field transformation capabilities of Electromagnetic (EM) metasurfaces for creating a variety of illusions using the concept of metasurface holograms. A system level approach to metasurface hologram synthesis is presented here, in which the hologram is co-designed with the desired object to be projected. A structured approach for the classification of the creation of EM illusions is proposed for better organization and tractability of the overall synthesis problem. The delineation is in terms of the initial incident (reference) illumination of the object to be recreated (front/back-lit), the position of illusion (posterior/anterior), and the illumination used to create the illusion (front/back). Therefore the classification is based on the specific relationship between the reference object to be recreated, the observer measuring the object, the orientation and placement of the reference and illumination field, and the desired placement of the metasurface hologram creating a virtual image. In the paper a general design procedure to synthesize metasurface holograms is presented based on Integral Equations (IE) and Generalized Sheet Transition Conditions (GSTCs), where the metasurface hologram is described as zero thickness sheet with tensorial surface susceptibility densities. Several selected configurations are chosen to illustrate various aspects of the hologram creation in 2D, along with a novel numerical technique to artificially reverse-propagate the scattered fields, required in the synthesis process. Finally, the impact of the metasurface size and the illumination field strength on the quality of the reconstructed scattered fields is also discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.001 |
| 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 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".