A 312pJ°C<sup>2</sup> Ultra-Low-Power Direct-ADC Multi-Range Temperature Sensor for IoT Nodes
Bibliographic record
Abstract
An ADC-direct energy-efficient temperature sensor with a novel multi-range BJT-based transducer design is presented. Thanks to the novel transducer design, the proposed NPN-based sensor does not require any analog-domain pre-ADC signal conditioning. Through bias current control, the sensing range can be adjusted for different applications. Our simulation results show a 2.67 × improvement in ADC dynamic range utilization for the military (-55° C to 125° C) and 3.8 × improvement for the medical (0°C to 100° C) temperature ranges, respectively. An Incremental ΔΣ-based readout circuitry for the proposed sensor has also been implemented and the circuit- and system-level simulation results are presented. The entire design draws 2.6μA from a 1.2V supply voltage to yield a resolution of 0.01° for a conversion time of 100ms, achieving a resolution FoM of 3.12pJ°C2.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".