Terahertz solid immersion microscopy: Recent achievements and challenges
Bibliographic record
Abstract
Unique effects of terahertz (THz)-wave–matter interaction push rapid progress in THz optoelectronics aimed at bridging the problematic THz gap. However, majority of modern methods of THz spectroscopy and imaging are still hampered by low spatial resolution. Common lens/mirror-based THz optics fails to overcome the Abbe barrier and usually provides resolution larger than a free-space wavelength λ (i.e., hundreds of micrometers or even few millimeters). To mitigate this difficulty, supperresolution THz imaging modalities were introduced recently, among which we particularly underline different methods of THz scanning-probe near-field microscopy. They not only rely on strong light confinement on sub-wavelength probes and provide resolution down to ∼10−1–10−3λ but also suffer from small energy efficiency or presume an interplay among imaging resolution, signal-to-noise ratio, and performance. In this paper, we consider reflection-mode THz solid immersion (SI) microscopy that offers some compromise between the high imaging resolution of 0.15λ and high energy efficiency, which is due to the absence of any subwavelength probe in an optical scheme. Recent achievements, challenging problems, and prospects of SI microscopy are overviewed with an emphasis on resolving the inverse problem and applications in THz biophotonics.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".