Photonic dot-product engine for optical signal and information processing
Bibliographic record
Abstract
Abstract In this paper, a novel 2 × 2 Mach–Zehnder-interferometer (MZI)-based dot-product calculator is proposed and analyzed in the silicon-on-insulator (SOI) platform. To calculate the dot product, a phase-shifted Bragg grating (PSBG) modulator is placed in each arm of the MZI for the phase modulation at the resonant wavelength, followed by a 3 dB 2 × 2 directional coupler (DC) as the output to convert the phase difference into the intensity distribution across bar and cross ports. Moreover, an electro-absorption modulator (EAM) is implemented between the PSBG and the DC to change the intensity of the passing light in both arms. Theoretical modeling shows that by adjusting the phase difference and absorption strength individually, multiplication of two input values can be achieved using the proposed design. Numerical analysis over 10 000 dot-product operations with 7 bit precision for input values reveals a mean squared error (MSE) of 2.67 × 10−5. By cascading multiple proposed designs operated at different wavelengths, vector-by-vector multiplication can be realized in parallel, leveraging the wavelength-division multiplexing (WDM) scheme and the Bragg reflection mechanism, with results superior to the current and prior MZI-based processors with coherent light sources. This design paves the way for large-scale optical information processing systems with high throughput.
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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 | 0.001 | 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".