A 300-GHz Dual-Polarized CMOS Waveguide Receiver for High-Density Terahertz Integrated Multichannel Wireless Systems-on-Chip
Bibliographic record
Abstract
This article presents a class of high-density waveguide dual-polarized receivers operating at terahertz (THz) frequencies, fully integrated on a single complimentary metal-oxide–semiconductor (CMOS) die. Unlike conventional TEM-mode dual-polarized receivers, which require doubled chip size for polarization diversity, our proposed nonTEM-mode substrate integrated waveguide (SIW) receiver features both vertical (V) and horizontal (H) polarization channels. This design is achieved through a 3-D on-chip integration that leverages the multilayer stack-up nature of advanced CMOS fabrication processes. The receiver, employing an interferometric receiving architecture, enables direct conversion of dual-polarized signals to baseband or intermediate frequency (IF) without increasing chip size, effectively halving the overall chip dimensions. This approach, particularly advantageous for large-scale integrated receiver arrays, also reduces topological complexity, structural loss, and power consumption. Consequently, the proposed receiver supports high data transmission throughput based on polarization diversity while maintaining the same chip footprint, marking a significant advancement for future high-density THz-integrated multichannel wireless systems. We designed, fabricated, and tested a proof-of-concept experimental prototype using a standard 65-nm CMOS process. Successful transmission and demodulation of a digitally modulated signal using M-quadrature amplitude modulation (M-QAM) at 300 GHz were achieved, supported by both simulated and measured results.
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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 | 0.001 |
| 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".