Broadband Terahertz Metal-Wire Signal Processors: A Review
Bibliographic record
Abstract
Communication links operating at terahertz frequencies are envisioned to provide a revolutionary enhancement of data transmission. As fundamental building blocks, waveguides play an indispensable role in future terahertz networks, not only transporting data streams with unprecedented data rates, but also serving as a versatile platform for signal processing. Among various terahertz waveguides, metal-wire waveguides have attracted particular attention due to their distinct characteristics, such as structural simplicity, broad operating bandwidths, low transmission losses, and low dispersion, in turn making them promising candidates for signal processing. However, because of the tight confinement of modal energy within the wavelength-scale space, manipulating the propagating terahertz signals in-between the metal-wires is challenging. Here, we report the most recent advances in the realization of signal-processing functionalities within metal-wire waveguides. Based on these state-of-the-art methodologies, broadband signal processors that can function as filters, couplers, temporal integrators, as well as multiplexers, have been obtained. We expect this review to inspire new terahertz metal-wire signal processors with high potential for real-time tunability and reconfigurability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".