Sub-mK, On-Package Temperature Control for High-Performance Microsystem Applications
Bibliographic record
Abstract
High-precision sensing applications require stable operating environments for the components, with temperature stability often being the most critical requirement. Existing methods for temperature control rely on a combination of device level (i.e., on-die) and system level (i.e., on-board and on-enclosure) temperature control and shielding to achieve the necessary temperature stability. However, die-level solutions offer limited postfabrication flexibility, while board/enclosure-level solutions contribute to increased system bulk and power consumption. Herein, we demonstrate a simple and versatile method of using on-package heaters and sensors for temperature control. Various configurations of surface-mount heaters and sensors were examined around a commercially available ceramic package. A simple proportional-integral temperature controller was developed and used for integrated temperature control. We demonstrate that the method achieves similar accuracies to having an on-die sensor, with sub-mK temperature stability achieved with a power consumption of approximately 500 mW. The presented approach offers a power-efficient, compact solution for precision temperature control that is applicable to a broad range of high-performance sensing applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".