A Low-Loss, 77 GHz, 8 × 8 Microstrip Butler Matrix on a High-Purity Fused-Silica (HPFS) Glass Substrate
Bibliographic record
Abstract
A low-loss, compact, ultra-thin, passive, 77 GHz, 8 × 8 microstrip Butler matrix on a 200 μm thick high-purity fused-silica (HPFS) glass substrate embedded in 0.8 μm thick patterned gold conducting layers was developed for low power automotive radars. The first-of-its-kind, HPFS, glass-based Butler matrix comprised 12 hybrid couplers, 16 crossovers, and 8 phase shifters in a footprint area of 19.1 mm × 26.6 mm. The device and the corresponding building blocks were designed and optimized using 3D electromagnetic finite element method (FEM) simulations using the Advanced Design System (ADS) from Keysight™ Technologies. Due to the very-low-loss tangent of the HPFS glass substrate (0.0005 @77 GHz) compared to other common substrate materials and rigorous design optimization, the return loss and isolation of the input ports are both below -20 dB, respectively, as verified by 3D FEM simulations. Due to the absence of any published data on a 77 GHz 8 × 8 Butler matrix, the design was validated by developing a 4 × 4 version of the Butler matrix using the same building blocks and comparing the 3D simulation results in ADS with results published elsewhere that showed that the developed Butler matrix offers lower insertion loss in a 10% smaller footprint area. A low-cost microfabrication method has been developed to fabricate the devices using a standard lift-off process. A scaled version of the device can be used for 5G beamforming applications.
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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".