Near-Infrared Electronically-Controlled Variable Attenuator for W-Band Frequency Range
Bibliographic record
Abstract
This article presents the design, fabrication, and performance evaluation of a novel high-performance millimeter-wave (mm-wave) variable attenuator operating across the 75–110 GHz frequency band, controlled via near-infrared (NIR) illumination. The device achieves a maximum attenuation range of 50 dB with precise control steps as small as 0.1 dB. A high-resistivity (HR) silicon-based image waveguide, with a resistivity of$\rho \gt 10$k$\Omega \cdot $cm, serves as the transmission line, operating within the WR10 rectangular waveguide standard. The silicon image waveguide is seamlessly integrated with WR10 metallic rectangular waveguides at both input and output through optimized metallic fixtures. Its dimensions are tailored to support single-mode$\textrm {E}_{11}\textrm {y}$operation. Attenuation control is achieved using three NIR light-emitting diodes (LEDs) with a total power consumption of 21 mW at a wavelength of 870 nm. The NIR illumination generates charge carriers in the silicon waveguide, increasing its conductivity and thereby attenuating the mm-wave signal with minimal phase distortion. The generated carrier mechanism in silicon is based on the absorption of near-infrared (NIR) photons. As a result, charge carriers are excited from the valence band to the conduction band, leading to a change in conductivity. This attenuation mechanism provides levels up to 50 dB and ensures precise, energy-efficient operation. The measured return loss (S11) is higher than 17 dB throughout the frequency range of 75 to 110 GHz. The compact and robust structure of the attenuator makes it ideal for emerging millimeter-wave (mm-wave) and low-terahertz (THz) wireless communication systems and phased array applications. It is worth mentioning that the designed attenuator can also function as an mm-THz modulator. By carefully selecting a short carrier lifetime in doped silicon and choosing suitable LEDs, it is indeed possible to significantly increase the modulation rate.
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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 | 0.001 | 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".