Linear arrays of microbolometers for space applications
Bibliographic record
Abstract
Space-grade linear arrays of microbolometers are a new class of sensors intended for pushbroom scanned remote sensing and atmospheric sounding with limited power and mass budgets. Such applications call for: (i) microbolometers with increased speed and detectivity; (ii) radiation hard CMOS circuits allowing for simultaneous data readout from all microbolometers; and (iii) robust radiometric packages. A series of single stage and double stage microbolometers with thermal conductances from 30 to 250 nW/K have been investigated. The time constants measured on single stage and double stage devices were in good agreement with the conductances, showing values as small as 6 ms. Low frequency detectivities were found to be in the range from 108to 109cm.Hz1/2/W. Arrays of 512x3 microbolometers were monolithically built on CMOS circuits laid out such that all pixels can be read in parallel with their own signal chain. When integrated into a custom designed radiometric package, these arrays exhibit NETD values better than 80 mK to a 300 K scene using F/0.87 optics in the 8–12 um band and 50 ms integration time. The preliminary environmental tests performed on arrays integrated into a custom designed radiometric package indicated that they would meet the requirements for space operation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".