Successful MWIR FPA fabrication using gas cluster ion-beam InSb surface finishing
Bibliographic record
Abstract
As the demand for mid wavelength infrared (MWIR) focal plane arrays (FPAs) continues to increase, the quality of InSb surfaces becomes more stringent. State-of-the art InSb contains <20 etch pits/cm2 (EPD), and provides a surface suitable for rapid oxide desorption and high quality MBE growth. In order to satisfy resolution and sensitivity requirements for advanced MWIR FPA imaging systems ( 1 to 5.4 μm region @77°K), the surface and sub-surface of the material must be of excellent quality. CMP has proven to be a qualified finishing process for InSb surfaces in the fabrication of IRFPAs. However, a time consuming surface etch is universally required in the IRFPA manufacturing process. Gas cluster ion beam processing (GCIB) has been shown to significantly enhance the surface oxide desorption of both GaSb and InSb substrates for MBE growth and provides an alternate surface finish for IRFPA manufacturing. The use of GCIB may preclude the need for surface etching, thus reducing IRFPA processing time and chemical cleanup. This study examines the comparison of CMP and GCIB finishes on InSb surfaces and the effect on final IRFPA device pass rates. NF3/O2 dual energy GCIB surface processing was used in this study. Atomic force microscopy (AFM), cross-section transmission electron microscopy (XTEM), and rocking curve x-ray diffraction (XRD) examine the surface and subsurface InSb integrity. A comparison of pass-rates for completed IRFPAs with the CMP and GCIB surface shows the pass-rate to be the same, opening the possibility for etch step elimination.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".