An All-Digital Time-Domain Smart Temperature Sensor With a Cost-Efficient Curvature Correction
Bibliographic record
Abstract
This paper presents an all-digital time-domain smart temperature sensor (TDSTS) with a cost-efficient curvature correction, which can effectively improve the accuracy and cost. Inverter-based TDSTSs are low cost and low complexity but they exhibit a significant curvature, which severely impairs their accuracy. Linearity enhancement through linear interpolation reduces the curvature and results in a twofold improvement in the accuracy. In this paper, the proposed curvature correction not only simplified the scheme to reduce the hardware cost but also successfully eliminated the curvature to achieve the most suitable accuracy. In addition, the correction procedure was also simplified. The TDSTS was implemented in eight Xilinx field-programmable gate arrays for performance verification and occupied 74 slices per sensor. The logic utilization for the proposed curvature-corrected circuit was 17 slices, and its cost was only 23% of the hardware cost. The experimental results indicated that the maximum inaccuracy decreased from 5 °C to 1.8 °C over the temperature range of -20 °C-100 °C, resulting in an approximate threefold improvement in the accuracy. Compared with a previous study, the improvements of 30% and 43% in the accuracy and cost, respectively, successfully validate the proposed technique.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".