Wafer-level radiometric performance testing of uncooled microbolometer arrays
Bibliographic record
Abstract
A turn-key semi-automated test system was constructed to perform on-wafer testing of microbolometer arrays. The system allows for testing of several performance characteristics of ROIC-fabricated microbolometer arrays including NETD, SiTF, ROIC functionality, noise and matrix operability, both before and after microbolometer fabrication. The system accepts wafers up to 8 inches in diameter and performs automated wafer die mapping using a microscope camera. Once wafer mapping is completed, a custom-designed quick insertion 8-12 μm AR-coated Germanium viewport is placed and the chamber is pumped down to below 10-5 Torr, allowing for the evaluation of package-level focal plane array (FPA) performance. The probe card is electrically connected to an INO IRXCAM camera core, a versatile system that can be adapted to many types of ROICs using custom-built interface printed circuit boards (PCBs). We currently have the capability for testing 384x288, 35 μm pixel size and 160x120, 52 μm pixel size FPAs. For accurate NETD measurements, the system is designed to provide an F/1 view of two rail-mounted blackbodies seen through the Germanium window by the die under test. A master control computer automates the alignment of the probe card to the dies, the positioning of the blackbodies, FPA image frame acquisition using IRXCAM, as well as data analysis and storage. Radiometric measurement precision has been validated by packaging dies measured by the automated probing system and re-measuring the SiTF and Noise using INO’s pre-existing benchtop system.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".