Design and Numerical Studies of Optical Alignment Rulers for Layer-by-Layer Integration
Bibliographic record
Abstract
Plasmonics structures have gained great attention by research since these structures could be engineered to manipulate light into a unique fashion. They allow the coupling of the incident radiation with the surface electrons on the metal surface of the structure. This coupling has been utilized in a variety of applications including structural coloring, imaging, sensing, and security. In this research, a study of a potential alignment technique based on a plasmonic structure is proposed and designed by incorporating nano-optical technology for possible uses in vertical integration of device layers in 3D ICs technology to boost the performance while maintaining small form factor. The structures are positioned onto the layers that need to be integrated. TE (Transverse Electric) and TM (Transverse Magnetic) modes are used to achieve the accurate integration and alignment: light blocking mode only and light blocking with plasmonics mode. In the light blocking mode, incident light is s-polarized and is used to guide the horizontal alignment where light intensity changes are very sensitive to the small nano physical shifts. In the other mode, with consideration of surface plasmon excitation, incident light is p-polarized and is used to guide the complete alignment where light transmission peaks are observed. To validate these modes, numerical studies are carried using simulations and presented here.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".