INVESTIGATION OF THE EFFECT OF SAMPLE HEIGHT AND SURFACE CURVATURE ON THE ANTI-REFLECTION PROPERTIES OF DLC COATINGS ON Si SUBSTRATES USING PECVD TECHNOLOGY
Bibliographic record
Abstract
In the field of security, thermal imaging devices are increasingly employed in surveillance systems and often operate under harsh environmental conditions. To ensure their durability and performance, protective diamond-like carbon (DLC) coatings are often deposited on the outer lens or protective lens. DLC films are highly attractive owing to their excellent wear resistance, high hardness, environmental stability, and infrared transparency within the spectral range of thermal imaging systems. In this study, DLC coatings were deposited using plasma-enhanced chemical vapor deposition (PECVD) with the Aegis DLC-PECVD system (Intlvac, Canada). A challenge in coating large optical components is that variations in plasma distribution can lead to film non-uniformity. We report experimental results on the deposition of anti-reflective DLC coatings on Si substrates for the mid-wave infrared spectral region (3-5 µm). The uniformity of the DLC layers was assessed through transmission spectroscopy of samples placed at different positions inside the deposition chamber. The results indicate that varying the sample height from 0 to 40 mm relative to the plasma electrode caused negligible changes in spectral shape, while the transmission intensity exhibited only a slight variation of ~1.6% for single-side DLC-coated samples. For samples placed at different tilt angles, the transmission peak shifted toward longer wavelengths, from 3.4 to 4 µm, corresponding to incident angles between 0° and 50°. These findings provide useful insights for optimizing sample holder design to improve the thickness uniformity and optical performance of DLC coatings on large thermal imaging lenses.
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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 | 0.001 |
| 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".