Effect of Oscillator Phase Noise on Synchronous Demodulation Measurement Systems for Sensing Applications
Bibliographic record
Abstract
Synchronous demodulation is a well-established technique for precise definition and reduction of noise bandwidth in various applications. Typically the noise added by the nonlinear elements such as reference oscillator and demodulator is assumed to be negligible. However, with the scaling of the dimensions of micro-sensors, the signals from these sensors generally tend to become weaker. Therefore, there is a need to study the so far neglected noise contributions from the components of the synchronous demodulator. In this paper, we focus on the significance of the phase noise of the reference oscillator on the system performance. A detailed analytical model is developed to investigate the nonlinear interaction. It is shown that the phase noise of the reference oscillator can significantly contribute to the output noise depending on the phase difference between the reference and measured signals. Close-to-resonance phase noise components produce low- frequency components at the output. However, our experimentally tested analysis indicates that the effect of phase noise on the output signal can be eliminated by complete compensation of the phase shift between reference and measured signals. This study applies to the design of low cost, high-performance measurement systems for high precision sensor 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".