A 26-GΩ Input-Impedance 112-dB Dynamic-Range Two-Step Direct-Conversion Front-End With Improved Δ-Modulation for Wearable Biopotential Acquisition
Bibliographic record
Abstract
This article presents a high dynamic range (DR) direct conversion front-end (Direct-FE) IC enabling the wearable acquisition of weak bio-potentials superposed onto large motion artifacts (MAs). The prototype IC has been fabricated in a standard 0.18-$\mu $m CMOS process. Benefiting from the proposed feedback (FB) two-step direct conversion architecture with an improved$\Delta $-modulation, as well as a novel differential difference amplifier (DDA) and a dynamic-element-matching (DEM) technique, it achieves a peak input range of 3.56 VPP, an input-referred noise (IRN) of$2.2~{\mu }$Vrms, an input impedance of 26 G$\Omega $, and a ±1.8-V electrode dc offset (EDO) tolerance, while consuming only 63-$\mu $W power. Compared with state-of-the-art Direct-FEs, the proposed work demonstrates an advanced DR (112 dB) and a competitive FOMDR (175 dB). The prototype IC has been validated based on in vivo experiments, demonstrating its capability for artifact-tolerant wearable bio-potential acquisition.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".