Monitoring Quality of ATLAS ITk Strip Sensors Through Database
Bibliographic record
Abstract
The High-Luminosity LHC upgrade necessitates a complete replacement of the ATLAS Inner Detector with a larger all-silicon tracker. The strip portion of it covers 165 m$^2$ area, afforded by the strip sensors. Following several prototype iterations and a successful pre-production, a full-scale production started in 2021, to finish in 2025. It will include about 21,000 wafers and a factor of 5 higher throughput than pre-production, with about 500 sensors produced and tested per month. The transition to production stressed the need to evaluate the results from the Quality Control (QC) and Quality Assurance (QA) tests quickly to meet the monthly delivery schedule. The test data come from 15 collaborating institutes, therefore a highly distributed system with standardized interfaces was required. Specialized software layers of QA and QC Python code were developed against the backend of the ITk database (DB) for this purpose. The developments included particularities and special needs of the Strip Sensors community, such as the large variety of different test devices and test types, the necessary test formats, and different workflows at the test sites. Special attention was paid to techniques facilitating the development and user operations, for example creation of “parallel” sets of dummy DB objects for practice purposes, iterative verification of operability, and the automatic upload of test data. The scalability concerns and automation of the data handling were included in the system architecture from the very inception. The full suite of functionalities include data integrity checks, data processing to extract and evaluate key parameters, cross-test comparisons, and summary reporting for continuous monitoring. We will also describe the lessons learned and the necessary evolution of the system.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".