Wavelength dependent vertical integration of nanoplasmonic circuits utilizing coupled ring resonators
Bibliographic record
Abstract
To become a competitor to replace CMOS-electronics for next-generation data processing, signal routing, and computing, nanoplasmonic circuits will require an analogue to electrical vias in order to enable vertical connections between device layers. Vertically stacked nanoplasmonic nanoring resonators formed of Ag/Si/Ag gap plasmon waveguides were studied as a novel 3-D coupling scheme that could be monolithically integrated on a silicon platform. The vertically coupled ring resonators were evanescently coupled to 100 nm x 100 nm Ag/Si/Ag input and output waveguides and the whole device was submerged in silicon dioxide. 3-D finite difference time domain simulations were used to examine the transmission spectra of the coupling device with varying device sizes and orientations. By having the signal coupling occur over multiple trips around the resonator, coupling efficiencies as high as 39% at telecommunication wavelengths between adjacent layers were present with planar device areas of only 1.00 μm2. As the vertical signal transfer was based on coupled ring resonators, the signal transfer was inherently wavelength dependent. Changing the device size by varying the radii of the nanorings allowed for tailoring the coupled frequency spectra. The plasmonic resonator based coupling scheme was found to have quality (Q) factors of upwards of 30 at telecommunication wavelengths. By allowing different device layers to operate on different wavelengths, this coupling scheme could to lead to parallel processing in stacked independent device layers.
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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 | 0.000 |
| 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 | 0.001 | 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".