A 200GΩ-Z<sub>IN</sub>, <0.2%-THD CT-△Σ-Based ADC-Direct Artifact-Tolerant Neural Recording Circuit
Bibliographic record
Abstract
Design, implementation, and post-layout validation of a DC-coupled chopper-stabilized continuous-time $\triangle\Sigma$-based ADC-direct artifact-tolerant neural recording circuit is presented. The architecture employs a dual fine-coarse first-order $\triangle\Sigma$ modulator to simultaneously record $\mu$V-level neural signals in the presence of DC offsets and differential artifacts up to ±140mV. The input transconductance stage (capable of handling rail-to-rail CM input with200G$\Omega$ input impedance for the entire frequency band of interest (DC-5kHz). Chopper stabilization is also conducted at the input to minimize flicker noise (Integrated IRN: 1.22$\mu V_{rms}$). Our transient simulation results show the circuit’s capability in differential artifact recovery under 200$\mu$s. Thanks to avoiding multi-bit capacitive/resistive DACs and using the same loop filter blocks for both neural recording and artifact compensation, a channel area of 0.035m$\text{m}^{2}$ is achieved, which is largely dominated by process-scalable digital blocks. The entire recording circuit consumes 5.4$\mu$W and yields an effective dynamic range of 50+40.9dB for neural signals and artifacts. The circuit’s performance comparison to the state of the art is also presented. keywords: Neural recording, stimulation artifact, high DR, ADC direct architecture, DC coupled input, artifact tolerant.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".