Compact Thermal and System Modeling of Chip-Scale Pyroelectric Infrared Imager
Bibliographic record
Abstract
The performance of pyroelectric infrared detectors is directly related to the ability of the sensor material to retain infrared energy (heat) incident from the source and to react fast to changing heat loads. This leads to a complicated, three dimensional, transient thermal models when many detectors are assembled into an infrared focal plane array (IRFPA) for thermal imaging. Adjacent pixels and the underlying substrate conduct heat away from the sensor material and add thermal mass to the system. This paper describes efforts and drawbacks in deriving a system model to capture thermal phenomena in a candidate IRFPA. Of particular interest is the tradeoff between cumbersome finite element models (long solve time, complicated meshes) and a reduced-size RC network circuit model that is simple to solve and integrate with the electrical design but may not capture the full thermal behavior of the system adequately. The thermal models are cast in terms of the operating principles of pyroelectric devices to describe a full electrical-thermal system model that adapts existing literature in the field to the specific system described in this work.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".