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 with <0.2% THD) is DC coupled to the electrode, achieving >200G$\Omega$ 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".