A 200-mV Linear Dynamic Range VCO-Based Readout Interface with Offset Compensation for Resistive Bridge Sensors
Bibliographic record
Abstract
This paper presents a high dynamic range (DR) VCO-based readout circuit for Wheatstone bridge sensors. VCO-based readout circuits often present non-linear voltage-to-frequency relationships and lack compensation circuitry. The proposed design offers high linearity by using a linear delay cell for the VCO core and linear differential voltage-to-current converter circuits to control the VCO oscillation frequency proportional to sensor output voltage changes. The frequency-adjustable core is introduced to compensate for temperature variations, drift, and DC offset in the resistor bridge sensor and in the circuit itself for higher performance. Thanks to both the proposed V-I converter and the core VCO, the circuit can provide a linear dynamic range of 200 mV, a phase noise of -113.7 dB at 1 MHz offset frequency, and a power consumption of 17 µW. Post-layout simulation in the 0.13-µm CMOS process demonstrates the effectiveness of the presented topology. The circuit features a tuning range of 117% with oscillation frequency of 6.13 MHz, temperature sensitivity of 0.0275 ppm/°C in the range of 25–40 °C, supply line sensitivity of 23ppm/V for 10% variation in the VDD, and a figure of merit of 150.4 dB, which highlights its ability to deliver accurate and reliable sensor readings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".