A current feedback instrumentation amplifier with chopping and dynamic element matching techniques and employing the current-reuse technique in input/feedback stages
Bibliographic record
Abstract
This paper presents a high-precision low-noise amplifier intended to be used in the front-end of an analog signal processing channel in sensor applications. A current feedback instrumentation amplifier (CFIA) with current-reuse (CR) topology employed chopping techniques to eliminate flicker (1/f) noise and offset due to amplifier, and the gain mismatch issue due to CR topology was solved by dynamic element matching (DEM) technique. This work was fabricated in a 0.35-μm CMOS TSMC process. After chopping the Gm1-Gmfband Gm2stages of CR-CFIA with 10kHz frequency, the input-referred noise power spectral density (PSD) was under 100nV/√Hz higher than 10kHz. The PSD effectively decreases below 100Hz. The gain of this system was 13. Total current supply was only 66.6μA. Total power consumption for analog block was 199.8μW under a 3-V supply and the core chip size was 0.81mm2(total area 3.01mm2).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".