Helling, C. et al, Bernabeu, J., Lacasta, C., & Solaz, C. (2020). Strip sensor performance in prototype modules built for ATLAS ITk. Nucl. Instrum. Methods Phys. Res. A, 978, 164402–6pp.
Abstract: ATLAS experiment is preparing an upgrade of its detector for High-Luminosity LHC (HL-LHC) operation. The upgrade involves installation of the new all-silicon Inner Tracker (ITk). In the context of the ITk preparations, more than 80 strip modules were built with prototype barrel sensors. They were tested with electrical readout on a per-channel basis. In general, an excellent performance was observed, consistent with previous ASIC-level and sensor-level tests. However, the lessons learned included two phenomena important for the future phases of the project. First was the need to store and test the modules in a dry environment due to humidity sensitivity of the sensors. The second was an observation of high noise regions for 2 modules. The high noise regions were tested further in several ways, including monitoring the performance as a function of time and bias voltage. Additionally, direct sensor-level tests were performed on the affected channels. The inter-strip resistance and bias resistance tests showed low values, indicating a temporary loss of the inter-strip isolation. A subsequent recovery of the noise performance was observed. We present the test details, an analysis of how the inter-strip isolation affects the module noise, and the relationship with sensor-level quality control tests.
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Fernandez-Tejero, J., Bartl, U., Docke, M., Fadeyev, V., Fleta, C., Hacker, J., et al. (2020). Design and evaluation of large area strip sensor prototypes for the ATLAS Inner Tracker detector. Nucl. Instrum. Methods Phys. Res. A, 981, 164536–6pp.
Abstract: The ATLAS community is facing the last stages prior to the production of the upgraded silicon strip Inner Tracker for the High-Luminosity Large Hadron Collider. An extensive Market Survey was carried out in order to evaluate the capability of different foundries to fabricate large area silicon strip sensors, satisfying the ATLAS specifications. The semiconductor manufacturing company, Infineon Technologies AG, was one of the two foundries, along with Hamamatsu Photonics K.K., that reached the last stage of the evaluation for the production of the new devices. The full prototype wafer layout for the participation of Infineon, called ATLAS17LS-IFX, was designed using a newly developed Python-based Automatic Layout Generation Tool, able to rapidly design sensors with different characteristics and dimensions based on a few geometrical and technological input parameters. This work presents the layout design process and the results obtained from the evaluation of the new Infineon large area sensors before and after proton and neutron irradiations, up to fluences expected in the inner layers of the future ATLAS detector.
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Bach, E. et al, Bernabeu, J., Lacasta, C., Solaz, C., & Soldevila, U. (2024). Analysis of the quality assurance results from the initial part of production of the ATLAS18 ITK strip sensors. Nucl. Instrum. Methods Phys. Res. A, 1064, 169435–8pp.
Abstract: The production of strip sensors for the ATLAS Inner Tracker (ITk) started in 2021. Since then, a Quality Assurance (QA) program has been carried out continuously, by using specific test structures, in parallel to the Quality Control (QC) inspection of the sensors. The QA program consists of monitoring sensor-specific characteristics and the technological process variability, before and after the irradiation with gammas, neutrons, and protons. After two years, half of the full production volume has been reached and we present an analysis of the parameters measured as part of the QA process. The main devices used for QA purposes are miniature strip sensors, monitor diodes, and the ATLAS test chip, which contains several test structures. Such devices are tested by several sites across the collaboration depending on the type of samples (non-irradiated components or irradiated with protons, neutrons, or gammas). The parameters extracted from the tests are then uploaded to a database and analyzed by Python scripts. These parameters are mainly examined through histograms and timeevolution plots to obtain parameter distributions, production trends, and meaningful parameter-to-parameter correlations. The purpose of this analysis is to identify possible deviations in the fabrication or the sensor quality, changes in the behavior of the test equipment at different test sites, or possible variability in the irradiation processes. The conclusions extracted from the QA program have allowed test optimization, establishment of control limits for the parameters, and a better understanding of device properties and fabrication trends. In addition, any abnormal results prompt immediate feedback to a vendor.
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