Wafer-level radiometric performance testing of uncooled microbolometer arrays
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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<sup>-5</sup> 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it