3D-printed demultiplexer circuits using suspended-in-air grating couplers for terahertz communications
Bibliographic record
Abstract
Integrated photonic circuits are in great demand for the upcoming THz communications. This work explores 3D printing to realize high-quality, high-refractive-index-contrast integrated components and devices for demultiplexing terahertz channels within the Wavelength Division Multiplexing modality. Namely, by printing integrated circuits using Polypropylene filaments suspended in air, we profit from the high-refractive index contrast of such a material combination to realize relatively compact low-loss waveguides, bends, couplers, and fiber Bragg gratings. The two-nozzle FDM printer allows simultaneous printing with filaments of two distinct sizes of 800um and 400um, with the larger filament used to make waveguides and couplers, and the smaller one used to define high-quality fiber Bragg gratings containing as much as 100 periods and featuring stop bands as wide as 10 GHz. Furthermore, by employing judiciously designed mechanical supports we show how to integrate such subcomponents into functional components such as single-channel drop filters. Finally, we developed a low-loss splicing technique for joining several components into functional devices and demonstrated four-channel THz WDM demultiplexers with in-plane (horizontal) and a more compact out-of-plane (vertical) integration. Experimentally, three-channel demultiplexers of THz signals with individual data rates up to 6 Gbps were demonstrated. Using finite element numerical modeling, integrated circuits were optimized for operation in the 120-165 GHz frequency band featuring ~5 GHz individual channel bandwidths and ~3 GHz inter-channel spectral spacing, and good agreement with the experiments was observed. We believe that the suspended-in-air integrated terahertz circuits hold strong potential for developing various linear optic transformers that will play a key role in energy-efficient analog processing of data streams for the upcoming terahertz communications. This is because of the high quality of the resultant circuits, ease of fabrication, and low infrastructure costs necessary for their manufacturing, thus allowing low-cost fast turnaround prototyping and development of terahertz signal processing devices even with the simplest 3D printing systems.
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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 | 0.000 |
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".