Hot spot visual evaluation of breakdown locations in ATLAS18 ITk strip sensors and test structures
Bibliographic record
Abstract
An important characteristic of silicon-based particle detectors, such as those used for the forthcoming ATLAS ITk upgrade for the HL-LHC, is the leakage current. This characteristic is evaluated in the quality control stage of the new ITk strip sensors by performing an IV measurement, where the sensors are biased up to − 700 V , typically showing low and stable leakage current. However, some sensors can exhibit a sudden leakage current increase during the IV measurement, so-called early breakdown, making the sensor unusable. The analysis of these early breakdown conditions typically consists of visual inspection of the sensors using a microscope, as often this is caused by physical damage, such as a deep scratch, chipping on the edges of the sensor, or other damage. However, the association of the observed damage with the early breakdown is not definitive. Rather, this is an association by correlation, due to the limits of verification by observation with standard equipment. A hot spot imaging setup has proven to be a valuable diagnostic tool to identify and understand these early breakdown conditions and elaborate on former understandings of these emissions. The regions responsible for the breakdown can be properly located by imaging the near infra-red light emissions produced by them in breakdown conditions. These regions of interest can also be imaged at magnification to evaluate the more precise structure of the breakdown to better understand the damage. The regions discovered, which have improved our understanding of breakdown damage and its symptoms, include scratches, chipping, static charge buildup and fabrication defects in the ATLAS18 strip sensors and test structures.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".