Integrated nanoplasmonic waveguides for magnetic, nonlinear, and strong‐field devices
Bibliographic record
Abstract
Abstract As modern complementary‐metal‐oxide‐semiconductor (CMOS) circuitry rapidly approaches fundamental speed and bandwidth limitations, optical platforms have become promising candidates to circumvent these limits and facilitate massive increases in computational power. To compete with high density CMOS circuitry, optical technology within the plasmonic regime is desirable, because of the sub‐diffraction limited confinement of electromagnetic energy, large optical bandwidth, and ultrafast processing capabilities. As such, nanoplasmonic waveguides act as nanoscale conduits for optical signals, thereby forming the backbone of such a platform. In recent years, significant research interest has developed to uncover the fundamental physics governing phenomena occurring within nanoplasmonic waveguides, and to implement unique optical devices. In doing so, a wide variety of material properties have been exploited. CMOS‐compatible materials facilitate passive plasmonic routing devices for directing the confined radiation. Magnetic materials facilitate time‐reversal symmetry breaking, aiding in the development of nonreciprocal isolators or modulators. Additionally, strong confinement and enhancement of electric fields within such waveguides require the use of materials with high nonlinear coefficients to achieve increased nonlinear optical phenomenon in a nanoscale footprint. Furthermore, this enhancement and confinement of the fields facilitate the study of strong‐field effects within the solid‐state environment of the waveguide. Here, we review current state‐of‐the‐art physics and applications of nanoplasmonic waveguides pertaining to passive, magnetoplasmonic, nonlinear, and strong‐field devices. Such components are essential elements in integrated optical circuitry, and each fulfill specific roles in truly developing a chip‐scale plasmonic computing architecture.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".