Optical resonance transmission properties of nano-hole arrays in a gold film: effect of adhesion layer
Bibliographic record
Abstract
In this paper, we present a systematic study on the influence of composition of the adhesion layer between gold and a Pyrex substrate on the optical resonance transmission properties of nano-hole arrays in an optically thick gold film. Large nano-hole arrays with different hole periodicities in a square lattice arrangement were fabricated using Electron Beam Lithography using different adhesion layers (chromium, titanium, or etched adhesion layer). The fabricated nano-hole arrays were optically characterized using transmission spectroscopy. The optical performance of each nano-hole array was numerically simulated using a Finite Difference Time Domain (FDTD) method. The experiments and simulations revealed that the optical resonance transmission properties (i.e. the resonance wavelength, the spectral transmission modulation ratio, and the resonance bandwidth) of the nano-hole arrays depended highly on the composition and the thickness of the adhesion layer. The optical resonance bandwidths were larger for the nano-hole arrays with chromium or titanium adhesion layers. Also, a red-shift of the optical resonance peak was observed for nano-hole arrays with a metal adhesion layer compared to the corresponding nano-hole arrays with an etched adhesion layer, but the red-shift was greatest for the nano-hole array with the titanium adhesion layer. For adhesion layers of greater thickness, the optical resonance peaks were reduced in magnitude. Finally, nano-hole arrays with an etched adhesion layer had a significant blue-shift in the optical resonance peak and a narrower optical resonance bandwidth compared to nano-hole arrays with a titanium or a chromium adhesion layer. Consequently, a narrow optical resonance bandwidth characteristic of a nano-hole array with an etched adhesion layer can potentially enhance the spectral selectivity and offer improved optical performance.
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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 | 0.000 | 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) | 0.000 | 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".