Compact chopper-stabilized neural amplifier with low-distortion high-pass filter in 0.13µm CMOS
Bibliographic record
Abstract
A compact and low-distortion neural recording amplifier is presented. The amplifier consists of two stages of amplification using capacitive feedback to set a gain of 54dB. To minimize flicker noise in the 1st stage, internal chopping is utilized at the folded node of the OTA, resulting in flicker noise contribution from the input differential pair only. A low-distortion constant-VGSfeedback circuit to set a low frequency high-pass pole is introduced. It is less sensitive to the output swing than the conventional sub-threshold MOS circuit. The amplifier fabricated in a standard 1.2V 0.13µm CMOS technology occupies 125×175µm2and achieves an NEF of 4.4, an input-referred noise of 4.7µV over a 5kHz bandwidth, a CMRR of 75dB and a THD of −50dB for a 0.6V output swing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".