A neuromimetic ultra low-power ADC for bio-sensing applications
Bibliographic record
Abstract
A compact 8-bit analog-to-digital converter (ADC) targeted for bio-sensing applications in systems-on-chip is presented. In particular, the design and implementation of the ADC with operation similar to a natural neuron cell in that it produces actions potentials corresponding to a stimulus of sufficient strength is described. An energy-saving buffer by reducing its effective capacitance is proposed to achieve low power consumption, and a specially designed switch and calibration system were incorporated in the design to improve the integral non-linearity (INL) of the ADC. The circuit was implemented in a standard 0.18 mum CMOS process technology with a 1.5 V supply, and a compact core area of 0.05 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Post layout simulations reveal that for a full scale range input current of 16 muA, the ADC maintains a maximum differential non-linearity (DNL) and INL of less than 0.16 LSB and 0.41 LSB respectively. The ADC achieves an ultra low energy dissipation of 5.46 pJ/cycle when operated at a sampling rate of 500 kS/s. This energy consumption is one of the lowest ever reported to date.
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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".