Fully integrated hybrid multimode-multiwavelength photonic processor with picosecond latency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it