Analysis of the results from Quality Control tests performed on ATLAS18 Strip Sensors during on-going production
Bibliographic record
Abstract
The ATLAS experiment will replace its existing Inner Detector with the new all-silicon Inner Tracker (ITk) to cope with the operating conditions of the forthcoming high-luminosity phase of the LHC (HL-LHC). The outer regions of the ITk will be instrumented with ∼ 18000 ATLAS18 strip sensors fabricated by Hamamatsu Photonics K.K. (HPK). With the launch of full-scale sensor production in 2021, the ITk strip sensor community has undertaken quality control (QC) testing of these sensors to ensure compliance with mechanical and electrical specifications agreed with HPK. The testing is conducted at seven QC sites on each of the monthly deliveries of ∼ 500 sensors. This contribution will give an overview of the QC procedures and analysis; the tests most likely to determine pass/fail for a sensor are IV, long-term leakage current stability, full strip test and visual inspection. The contribution will then present trends in the results and properties following completion of ∼ 60% of production testing. It will also mention challenges overcome through collaborative efforts with HPK during the early phases of production. With less than 5% of sensors rejected by QC testing, the overall production quality has been very good.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".