Cryogenic Noise-Parameter Measurement and the Design of Cryo-CMOS Circuits
Bibliographic record
Abstract
In this thesis, research into the application of cryogenic-CMOS (cryo-CMOS) circuits for radio astronomy and quantum computing are discussed. The thesis consists of three contributions to the field of CMOS circuit design and measurement:1) a fully-automated cryogenic noise-parameter measurement system,2) the design and verification of a radio-astronomy-grade cryo-CMOS low-noise amplifier (LNA) for the Dominion Radio Astrophysics Observatory (DRAO) synthesis telescope in Penticton, British Columbia, Canada, and3) a noise-matched low-power oscillator architecture for quantum computing applications.The three contributions are discussed in detail and unique problems associated with cryogenic design and measurement are discussed. The cryogenic noise-parameter measurement system enables the characterization of low-noise amplifiers for radio astronomy and quantum computing applications. The system iscapable of measuring noise parameters of LNAs at temperatures from 15K to 325K for frequencies up to 6 GHz and achieves a noise-temperature uncertainty of less than 1 K. The designed cryo-CMOS LNA operates at 20K over a frequency band of 0.9-to-1.8 GHz and achieves an average noise temperature of 5.4 K, an average gain of 37.2 dB, and an input return loss of better than 10 dB while consuming 115mW of power. This shows that CMOS can achieve comparable RF performance to exotic technologies at cryogenic temperatures. The designed oscillator topology uses LNA design procedures to break the trade off between power consumption and phase noise. The oscillator oscillates at 10.74GHz and achieves a 6 dB improvement in phase noise and figure of merit as compared to a traditional cross-coupled LC oscillator. The circuit consumes 980 ?W of power, and achieves a phase noise of -115.5 dBc/Hz at 1-MHz offset resulting in a figure of merit of 196.4 dBc/Hz. The research that makes up this thesis led to advancements that will enable the characterization of CMOS devices and circuits at cryogenic temperatures for RF applications. These advancements will create opportunities for the development of high sensitivity receivers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".