Optical Interconnects Finally Seeing the Light in Silicon Photonics: Past the Hype
Bibliographic record
Abstract
Electrical interconnects are becoming a bottleneck in the way towards meeting future performance requirements of integrated circuits. Moore's law, which observes the doubling of the number of transistors in integrated circuits every couple of years, can no longer be maintained due to reaching a physical barrier for scaling down the transistor's size lower than 5 nm. Heading towards multi-core and many-core chips, to mitigate such a barrier and maintain Moore's law in the future, is the solution being pursued today. However, such distributed nature requires a large interconnect network that is found to consume more than 80% of the microprocessor power. Optical interconnects represent one of the viable future alternatives that can resolve many of the challenges faced by electrical interconnects. However, reaching a maturity level in optical interconnects that would allow for the transition from electrical to optical interconnects for intra-chip and inter-chip communication is still facing several challenges. A review study is required to compare the recent developments in the optical interconnects with the performance requirements needed to reach the required maturity level for the transition to happen. This review paper dissects the optical interconnect system into its components and explains the foundational concepts behind the various passive and active components along with the performance metrics. The performance of different types of on-chip lasers, grating and edge couplers, modulators, and photodetectors are compared. The potential of a slot waveguide is investigated as a new foundation since it allows for guiding and confining light into low index regions of a few tens of nanometers in cross-section. Additionally, it can be tuned to optimize transmissions over 90° bends. Hence, high-density opto-electronic integrated circuits with optical interconnects reaching the dimensions of their electrical counterparts are becoming a possibility. The latest complete optical interconnect systems realized so far are reviewed as well.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".