Spatial resolution limit for a solid immersion lens
Bibliographic record
Abstract
The solid immersion (SI) effect is widely used to increase the spatial resolution of optical focusing systems and even overcome the Abbe diffraction limit. Resolution enhancement offered by a SI lens is mostly a function of its geometry and refractive index n SI . While SI lenses are relatively well understood, the scaling of the resolution enhancement by such lenses is still a subject of debate, with some works reporting ≃ n SI and ≃ n SI 2 dependencies for the hemispherical and hyperhemispherical SI lens configurations, respectively. In this paper, we offer a general argument for a resolution limit for SI optics and, then, verify it via the numerical analysis of the hemispherical and hyperhemispherical silicon SI lenses designed for the terahertz (THz) range. In fact, we find that there is no contradiction in the reported resolution enhancements ≃ n SI and ≃ n SI 2 ; however, they happen in different operation regimes. We then demonstrate that the resolution values reported for the different SI lens arrangements in the visible (VIS), near-, and middle-infrared (NIR and MIR), as well as THz bands obey the derived limit. Our findings will be useful for the further design and applications of SI optics.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".