Broadly tunable and ultra-highly selective detection of radio frequency signals enabled by a silicon photonic chip
Bibliographic record
Abstract
Precise and agile detection of radio frequency (RF) signals over an ultra-wide frequency range is a key functionality in modern communication, radar, and surveillance systems, as well as for radio astronomy and laboratory testing. However, current microwave solutions are inadequate for achieving the needed high performance in a chip-scale format, with the desired reduced cost, size, weight, and power. Photonics-based technologies have been identified as a potential solution but the need to compensate for the inherent noise of the involved laser sources have prevented on-chip realization of wideband RF signal detection systems. Here, we report an approach for ultra-wide range, highly-accurate detection of RF signals using a conceptually novel feed-forward laser's noise cancelling architecture integrated on chip. The technique is applied to realization of an RF scanning receiver as well as a complete radar transceiver integrated on a CMOS-compatible silicon-photonics chip, offering an unprecedented selectivity > 80 dB, spectral resolution < 1 kHz, and tunability in the full 0.5-35 GHz range. The reported work represents a significant step towards the development of integrated system-on-chip platforms for signal detection, analysis and processing in cognitive communication and radar network applications.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".