Additive manufacturing of highly reconfigurable plasmonic circuits for terahertz communications
Bibliographic record
Abstract
While in most existing terahertz communications systems, the THz carrier wave is transmitted via free-space channels, the THz waveguide-based integrated solutions can be of great utility at both the transmitter and receiver ends, thus simplifying the miniaturization and mass production of cost-effective THz communications systems. Here we present a new type of modular THz integrated circuits based on the two-wire plasmonic waveguide components fabricated using a combination of stereolithography (SLA) 3D printing, wet chemistry metal deposition, and hot stamping techniques. Particular attention is paid to the design of the optical circuits based on the two-wire waveguides suspended inside a protective micro-sized enclosure. Such waveguides feature low transmission and bending losses, as well as low dispersion. Using such waveguides as basic building blocks, we then demonstrate several key optical subcomponents, such as low-loss broadband 2×1THz couplers that use two coalescing two-wire waveguide bends, as well as broadband waveguide Bragg gratings that feature a paper sheet with a periodic sequence of metal strips inserted into the air gap of a two-wire waveguide. Finally, using these developed subcomponents, a two-channel add-drop multiplexer is demonstrated to operate at 140 GHz. We believe that the reported micro-encapsulated two-wire waveguide-based modular platform can have a strong impact on the field of THz signal processing and sensing due to the ease of device fabrication and handling, high degree of reconfigurability, and high potential for real-time tunability.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".