Commercial and custom 160x120, 256x1, and 512x3 pixel bolometric FPAs
Bibliographic record
Abstract
INO has been active in microbolometer and FPA technology development since the early 1990s. Microbolometer detectors based on VO2 films with TCR above 3% are typically fabricated. VOx films with TCR above 2% have been developed for applications where FPA temperature is not stabilized. INO is continuing its development of high fill factor pixels with sizes down to 25 micrometers and new macro- and micro-packaging technology. All fabrication is done on six inch wafers in INOs newly expanded clean room facility. INO currently offers as standard products 256x1 and 160x120 pixel FPAs with 52 micrometers pixel pitch. Both arrays have simple, robust, and versatile CMOS readout integrated circuits (ROICs) that may be accessed in self-scanning or random access mode, and reference detectors for on-chip coarse offset and temperature drift compensation. Single frame NETDs (f/1, 300 K, 8-12 micrometers ) are on the order of 150 - 250 mK and may be reduced by frame averaging. Prototyping boards have been developed for both arrays, and the 160x120 FPA has been integrated in a number of thermal cameras and instruments. In collaboration with its clients, INO has developed several FPAs for specific space and terrestrial applications. Custom ROICs fabricated in several different CMOS processes from multiple foundries have been used. A 512x3 pixel microbolometer FPA with 39 micrometers pitch is being developed for the European Space Agency. The array is designed for multi-spectral pushbroom imaging applications and features a novel ROIC with very low 1/f noise, pixel by pixel offset and drift compensation, variable integration time, and digital output. Its single frame NETD (f/1, 300 K, 8-12 micrometers ) is nominally 80 mK.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.020 | 0.006 |
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".