3D printed Fabry-Pérot filters for terahertz spectral range
Bibliographic record
Abstract
Abstract 3D printing is evolving into a standard tool for prototyping various optical components suitable for application in the terahertz range of the electromagnetic spectrum. This work takes the next step in this evolution by demonstrating the fabrication and subsequent evaluation of Fabry-Pérot Interferometers (FPIs). Large optical area (centimetre-scale) Fabry-Pérot transmission filters have been 3D printed with polylactic acid (PLA) using a commonly-used low-cost 3D printer. The advantages of the proposed approach include low cost, rapid prototyping, and repeatability. Terahertz transmission measurements for two demonstrated filter designs realised to target optimisation of either signal transmission or spectral filter performance have been performed using terahertz time-domain spectroscopy (THz-TDS) and demonstrate good agreement with the simulated response in the operating spectral band of 0.30 THz – 0.75 THz (wavelengths from 1,000 µm down to 400 µm). The critical spectral characteristics assessed were the filter peak transmission magnitude, central wavelength and full width at half-maximum (FWHM) of the transmission peak, as well as the free spectral range (FSR). The signal transmission levels were observed to reach beyond 90% for the first series of filters that targeted optimisation of this aspect; however, this was accompanied by diminished out-of-band rejection and broader transmission peaks in comparison to the second series of filters which targeted the overall performance. For the latter filter series, the resolution in terms of FWHM values of the transmission peaks was reduced to 40-50 GHz, with the out-of-band rejection approaching a ratio of 10:1. This level of spectral performance, along with the achieved signal peak transmission characteristics of 65-75%, provides adequate performance for many applications harnessing the terahertz spectral range.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".