Investigation of defects influencing performance of type-II InAs/GaInSb superlattice based infrared PIN type photodetectors
Bibliographic record
Abstract
We report on an investigation of dark current contributions from common microscale crystallographic defects in type-II InAs/GaInSb superlattice infrared PIN type photodiode structures grown on (100) GaSb substrates and have identified three general classifications. Defects on several wafers of varying design were examined from multiple perspectives to correlate electrical activity with structural properties, to develop the identification and classification scheme. Active defects were first identified by current density vs voltage (J-V) measurements and electron beam induced current (EBIC) scans of individual diodes with micrometer resolution. The EBIC scans were then correlated with plan-view optical and atomic force microscopy images, both before and after anisotropic etch-pit analysis using a newly developed etchant. The atomic scale structure of active and inactive defects was then compared using cross-sectional transmission electron microscopy (TEM) on vertical slices of defects extracted using focused ion beam milling. Analysis of the TEM images yielded important clues as to the structure and root causes of benign and active defects, in which only significant disruptions at the epi-substrate interface appear to play a key role in producing microscale defects that efficiently promote dark current.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".