A 25.8% 3σ/μ-Accuracy, 0.12%/°C Temperature Drift Sigma-Delta Modulation Calibrated Pseudo-Resistor With GΩ to TΩ Tuning Range
Bibliographic record
Abstract
This article presents an on-chip self-calibrated pseudo-resistor (PR) based on a sigma-delta modulation (SDM) loop. The proposed real-time calibration mechanism facilitates the implementation of an accurate low-drift ultra-high-value (UHV) PR with a wide tuning range at minimum hardware expenditure. Experimental results demonstrate that the resistance can be precisely tuned from 1.5 G$\Omega $to 2.5 T$\Omega $. The average temperature drift is 0.12%/°C within the temperature range from$- 40~^{\circ } $C to$80~^{\circ } $C, which is comparable to the on-chip high-resistance poly resistor. The relative accuracy ($3\sigma $/$\mu $) is 25.8% under room temperature, representing an improvement of over one order of magnitude compared to an uncalibrated PR. To validate the proposed calibration loop, a capacitively-coupled instrumentation amplifier (CCIA) embedding the calibrated PR has been fabricated in a standard 180-nm CMOS process, occupying a core area of 0.187 mm2. The CCIA achieves an accurately tunable high-pass corner frequency ($f_{\text {HP}}$) from 0.13 to 217 Hz, a total harmonic distortion (THD) as low as 0.0093%, and a linear output swing up to 3.9 VPP. The input-referred noise (IRN) is measured at$2.40~\mu $Vrmswithin a 0.5–200-Hz bandwidth. In conclusion, this work paves the way for implementing accurate tunable on-chip UHV resistors in mass production.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".