Towards on-chip photonic-assisted radio-frequency spectral measurement and monitoring
Bibliographic record
Abstract
Precise detection and monitoring of the frequency spectrum of microwave signals are essential to myriad scientific and technological disciplines, including both civil and defense areas, such as telecommunications, radar, biomedical instrumentation, radio astronomy, etc. Historically, microwave engineering has provided solutions for these tasks. However, current radio-frequency (RF) technologies suffer from inherent shortcomings that limit their capability to provide agile (e.g., real-time) measurements over a large operation bandwidth in energy-efficient and compact (e.g., integrated) formats. Overcoming these limitations is key to fulfilling pressing performance requirements in the above-mentioned application fields, as well as for compatibility with platforms that require chip-scale integration and/or low weight and dimensions, such as satellites and drones. Integrated microwave photonics is an emerging field that leverages the advantages of optical technologies for realization of microwave operations with high bandwidth, low power consumption, and increased agility and flexibility in on-chip platforms, offering an alternative path for integration of advanced RF processing and analysis methods in mature semiconductor technologies. This mini review surveys some of the latest advances in microwave spectral measurement and monitoring techniques realized through photonic approaches, with a special focus on methods suitable for on-chip integration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".