Integrated optical-to-optical gain in a silicon photonic modulator neuron
Bibliographic record
Abstract
Silicon photonic neural networks can achieve higher throughputs and lower latencies than digital electronic alternatives. However, recently reported implementations of such networks have lacked integrated signal gain, instead utilizing off-chip amplifiers or co-processors to complete the signal processing pipeline. Photonic neural networks without gain face substantial limitations in network depth and inter-layer fan-out. Here, we demonstrate a fully integrated silicon photonic modulator neuron capable of up to 14.1 dB gain, achieved by modeling and addressing self-heating behavior in our output PN-junction micro-ring modulator. We use our experimental neuron to emulate a small network subject to high loss, achieving superior accuracy on an automated modulation classification benchmark to that of an optimal linear system. Our high-gain neuron can serve as a building block vastly expanding the range of neural network architectures that can be implemented with silicon photonics.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".