Fully integrated hybrid multimode-multiwavelength photonic processor with picosecond latency
Bibliographic record
Abstract
High-speed signal processing is crucial for increasing the data throughput in next-generation communication systems, including multiple-input multiple-output (MIMO) networks, emerging 6G architectures, and beyond. However, system scaling inevitably increases hardware complexity, computational demands, and the challenges associated with digital signal processing (DSP). The physical limitations of electronic processors constrain computational throughput and increase DSP latency, creating a critical bottleneck. Photonic processors offer a compelling alternative, with inherent advantages of broad bandwidth, low loss, massive parallelism, and ultralow latency. Nevertheless, their scalability has been hindered by integration challenges, large device footprints, and on-chip multiplexing limits. Here, we present a scalable, monolithically integrated hybrid photonic processor that simultaneously leverages mode-division and wavelength-division multiplexing. The processor integrates adiabatic mode multiplexers, mode-selective microring resonators, and balanced multimode photodetectors on a single chip. We experimentally demonstrate real-time optical MIMO signal unscrambling at 5 Gb/s and radio frequency signal unjamming in phase-shift keying transmission, performed entirely in the analog optical domain with a processing latency of just 30 ps. This work opens a pathway toward energy-efficient, ultralow-latency processors for future wireless and optical communication networks.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".