Low noise, temperature‐compensated, electrochemical cell sigma–delta current measurement readout circuit
Bibliographic record
Abstract
Summary Nanopore ion channels are a promising solution for certain molecular structure analyses. Large arrays of nanopore channels and their associated readout circuits are used in many molecular studies such as DNA sequencing. Readout circuits must meet challenging performance criteria such as low noise operation, low power consumption, in‐channel digitization capability, and high linearity. Previously, sigma–delta modulators have been presented to address these criteria; however, their specifications show drifts with temperature. In this paper, an approach is presented to keep modulator performance constant with temperature variations. For this purpose, the sigma–delta modulator's feedforward and feedback branches are modified so that their gain coefficient remains constant over a certain temperature range. With large sensors arrays, solutions employing high bias currents in the feedback paths are not suitable due to power consumption limitations. Here, the design gives the possibility of switching low current levels in the feedback paths without affecting the ENOB. The proposed temperature compensation solution shows good performance when temperature is swept from 27°C to 100°C. Over the mentioned temperature range, the gain and bandwidth of the modulator show a change of less than 0.4%. It is further shown that for a 10 kHz input current signal with an amplitude of 600 pA, the ENOB and power consumption are 12.9 and 4.6 mW, respectively.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".