Broadband Achromatic Metalens With Enhanced Field of View for Thermal Image
Bibliographic record
Abstract
Despite the increasing interest in metalenses for imaging applications, there is a scarcity of research on metalenses designed to deliver broadband achromatic performance while preserving a wide field of view (FOV). In this work, we present a single-layer broadband achromatic metalens (BAM) designed for a specified FOV within a longwave infrared (LWIR) regime. Utilizing an advanced genetic algorithm coupled with diffraction theory, we optimize the BAM to minimize the coefficient of variation (CV) of focal lengths. The proposed BAM operates across wavelengths from 8 to 12 μm, facilitating an FOV of 40°. Our methodology is validated through the initial design of a BAM with a 200 μm aperture. Simulation outcomes reveal a low CV of 2.56% and an average focusing efficiency of over 40%. Furthermore, a 5 mm-diameter metalens prototype was fabricated and characterized by our custom measurement setup, which is both precise and less prone to subjective error than traditional testing methodologies. The prototype demonstrates an average focal length of 5.478 mm with minimal distortion at 2.18%. The Modulation Transfer Function (MTF) analysis confirms alignment with design specifications. Last, the imaging characterization of the metalens is carried out through experiments, demonstrating that the metalens can clearly resolve the measured object.
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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 | 0.000 |
| 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".