Silicon‐Integrated Next‐Generation Plasmonic Devices for Energy‐Efficient Semiconductor Applications
Bibliographic record
Abstract
Abstract Silicon (Si)‐based integrated photonics has demonstrated significant advances in miniaturization and performance, yet critical challenges remain in achieving efficient on‐chip communication at high bandwidths. This review asserts that next‐generation Si‐integrated plasmonics, particularly through advanced architectures like coupled hybrid plasmonic waveguides (CHPWs) and the strategic use of complementary metal–oxide–semiconductor (CMOS)‐compatible materials, offer a critical pathway to overcome these limitations. Plasmonic devices on Si and silicon‐on‐insulator (SOI) substrates enable subwavelength light confinement and enhanced light‐matter interactions through hybrid modes. However, integrating traditional plasmonic materials like gold (Au) and silver (Ag) into Si‐based platforms presents significant challenges, particularly due to their incompatibility with standard Si processing techniques and their increased optical losses at longer wavelengths, which can hinder performance in near‐infrared applications. Distinctively focusing on viable integration strategies, this review explores recent progress in Si‐integrated hybrid‐mode plasmonic devices, highlighting the potential of transparent conductive oxides (TCOs) like indium tin oxide (ITO) for low‐loss, tunable operation. Key device topologies, including CHPWs and dielectric‐based heterostructures, are examined in depth, alongside CMOS‐aligned fabrication techniques and practical considerations. By critically comparing various plasmonic approaches and identifying their respective advantages and limitations, a path toward realizing the full potential of plasmonics in shaping the future of high‐performance, Si‐based integrated photonics is charted.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".