Wafer-Scale Stitched CMOS Pixel Sensors: Characterisation and Detector Performance Studies for ALICE ITS3

Thesis

The ALICE experiment at the Large Hadron Collider at CERN will upgrade the innermost three layers of the currently installed Inner Tracking System (ITS2) to the ITS3 during Long Shutdown 3 (LS3), starting in 2026. The ITS3 introduces a groundbreaking detector design based on wafer-scale curved monolithic active pixel sensors. Each of the three silicon layers, thinner than 50~µm, will be bent around the beam pipe and supported by lightweight carbon-foam structures. The detector will be air-cooled. This configuration achieves an unprecedented material budget of just X/X₀ ≃ 0.09% per layer. This material reduction, combined with a decrease in the distance of the innermost tracking layer from the interaction point, results in a factor-of-two improvement in pointing resolution at transverse momenta below 10~GeV/c compared to the current detector. The ALICE core physics programme, requiring excellent secondary vertex reconstruction and low-mass tracking, will strongly benefit from this upgrade. To enable wafer-scale sensors up to 9.8 × 27 cm² , the design employs stitching -- a technique evaluated using the Monolithic Stitched Sensor (MOSS), a 14 × 259 mm² prototype fabricated in 65~nm CMOS technology. This work focuses on characterising MOSS in terms of fabrication yield, mechanical handling, and sensor performance. Custom hardware and software were developed for detailed characterisation, and a fault-diagnosis method was introduced to identify and resolve unexpected sensor failures. Finally, the performance of the final ITS3 in terms of pointing resolution and tracking efficiency was simulated assuming malfunctioning substructures on the sensor planes. Both qualitative and machine‑learning‑based approaches to optimising the geometrical arrangement of the layers are discussed, and the impact of reduced tracking efficiency on the Λ ⁺_c benchmark observable is investigated.

Authors: Eberwein, GH

• DOI: 10.5287/ora-1zg5rq0e0
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: physics simulation; silicon detectors; monolithic active pixel sensors; CMOS
• Subjects: Machine learning; Position sensitive particle detectors; Simulation methods; Failure mode and effects analysis; Particle beams, Colliding; Deep learning (Machine learning); Application-specific integrated circuits