Circuit and System Design for mm-wave Radar and Radio Applications
Bibliographic record
Abstract
Recent advancements in silicon technology have paved the way for the development of integrated transceivers operating well inside the mm-wave frequency range (30 - 300 GHz). This band offers opportunities for new applications such as remote sensing, short range radar, active imaging and multi-Gb/s radios. This thesis presents new ideas at the circuit and system level for a variety of such applications, up to 145 GHz and in both state-of-the-art nanoscale CMOS and SiGe BiCMOS technologies.\n\nAfter reviewing the theory of operation behind linear and power amplifiers, a purely digital, scalable solution for power amplification that takes advantage of the significant ft/fmax improvement in pFETs as a result of strain engineering in nanoscale CMOS is presented. The proposed Class-D power amplifier, features a stacked, cascode CMOS inverter output stage, which facilitates high voltage operation while employing only thin-oxide devices in a 45 nm SOI CMOS process.\n\nNext, a single-chip, 70-80 GHz wireless transceiver for last-mile point-to-point links is described. The transceiver was fabricated in a 130 nm SiGe BiCMOS technology and can operate at data rates in excess of 18 Gbps. The high bitrate is accomplished by taking advantage of the ample bandwidth available at the W-band frequency range, as well as by employing a direct QPSK modulator, which eliminates the need for separate upconversion and power amplification.\n\nLastly, the system and circuit level implementation of a mm-wave precision distance and velocity sensor at 122 and 145 GHz is presented. Both systems feature a heterodyne architecture to mitigate the receiver 1/f noise, as well as self-test and calibration capabilities along with simple packaging techniques to reduce the overall system cost.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".