Length dependent optical characteristics analysis for semiconductor nanowires
Bibliographic record
Abstract
Resonant optical mode excitations in semiconductor nanowires result in enhanced absorptions. Nominally, only the diameter dependent radial mode excitations have been considered for the increased absorption. In this paper, we try to understand how the length of the nanowires affects the resonant wavelength and peak absorption wavelengths. We answer two questions viz (1) at what minimum length are radial optical modes stabilized and dominate the absorption characteristics and (2) do longitudinal modes play a role in absorption characteristics especially in determining the resonant wavelength. Two different semiconductors are studied viz silicon and gallium arsenide. We find that even nanowires as short as 200 nm exhibit absorption characteristics dominated by the radial mode excitation. However, for lengths smaller than 200 nm, the optical characteristics are dominated by scattering. Further, we observe that longitudinal modes are excited in low absorption semiconductor materials like silicon for lengths up to 700 nm and the absorption peak depends both on the diameter and the wavelength. Further, shorter length nanowires may have higher absorption than the longer ones in this regime. We also observed that scattering from the nanowires is less than 2% of the incident light. For higher absorption semiconductor like GaAs, absorption characteristics are mainly determined by the radial mode excitations even for shorter lengths. The results provide further insight into the radial mode excitations in semiconductor nanowires.
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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 | 0.001 |
| 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".