Volume 420 - 10th International Workshop on Semiconductor Pixel Detectors for Particles and Imaging (Pixel2022) - Poster Session
Monitoring Quality of ATLAS ITk Strip Sensors Through Database
V. Fadeyev*, M. Beránek, E. Bach, M. Basso, A.J. Blue, P. Federicova, J. Fernandez-Tejero, A. Fournier, G. Greig, D. Hamersly, K. Hara, E. Hill, S. Hirose, B. Hommels, D. Jones, C.T. Klein, T. Koffas, V. Latonova, M. Mikestikova, K. Nakamura, L. Poley, R. Orr, D. Rousso, B. Stelzer, M. Sykora, M. Ullan and Y. Unnoet al. (click to show)
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Pre-published on: March 13, 2023
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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.
DOI: https://doi.org/10.22323/1.420.0058
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