Temporal point-by-point arbitrary waveform synthesis beyond tera sample per second
Bibliographic record
Abstract
Arbitrary waveform synthesizers are indispensable in modern information technology, yet electronic counterparts are limited by the speed of analog-to-digital converters to hundreds of GSa/s. While photonic-assisted synthesizers offer potential to surpass this ceiling, scalability and reconfigurability remain challenges. Here, we propose a temporal point-by-point arbitrary waveform synthesizer beyond TSa/s, leveraging an optical temporal Vernier caliper in the photonic synthetic dimension. The system, combining a mode-locked laser and a fiber loop, controls the sampling rate of synthesized waveforms by exploiting a slight detuning between the pulse period and the round-trip delay of the fiber loop. The experiment demonstrates generated waveforms with ultra-high, tunable sampling rate up to 1 TSa/s, an order of magnitude higher than state-of-the-art electronic counterparts. Additionally, the system supports up to 10.4 kilo-points in memory depth. As application examples, the generation of communication waveforms for high-speed wireless communications and linearly chirped microwave waveforms for high-resolution multi-target detection is demonstrated. Here the authors demonstrate a temporal Vernier caliper for arbitrary waveform synthesis with a sampling rate over 1 TSa/s. These results, which surpass current electronic systems by an order of magnitude, have potential applications in radar and high-speed communications.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".