Analysis of Sensitivity and Power Consumption of Chopping Techniques for Integrated Capacitive Sensor Interface Circuits
Bibliographic record
Abstract
In this paper, parameters related to the sensitivity of the interface circuits for capacitive sensors are determined. Both the input referred noise and capacitance of the input transistors are important for capacitive sensitivity. Chopping is an effective technique for signal conditioning circuits because of its capability of reducing circuit noise at low frequencies. The capacitive sensitivity and power consumption of various chopping techniques including the dual chopper amplifier (DCA), single chopper amplifier (SCA) and two-stage single chopper amplifier (TCA) are extracted for different values of total gain and sensor capacitance. The minimum sensitivity for each technique will be extracted based on the gain and sensor capacitance. It will be shown that designation of the amplifier and distribution of gain in the TCA and DCA are important for sensitivity. A design procedure for chopper amplifiers that illustrates the steps required to achieve either the best or the desired sensitivity while minimizing power consumption will be presented. It will be shown that for a small sensor capacitance and large total gain, the DCA has the best sensitivity, while for a large sensor capacitance and a lower gain, the SCA is preferable. The TCA is the desired architecture for an average total gain and a large sensor capacitance. Moreover, when the power consumption is the key requirement and the maximum sensitivity is not the goal; the TCA works best due to its potential to decrease the power consumption.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".