Speaker
Description
The ALICE ITS3 upgrade represents a major step forward in silicon tracking, employing the commercial 65 nm CMOS imaging technology (TPSCo) to develop ultra-thin monolithic active pixel sensors (MAPS) for high-energy physics applications. The target performance includes spatial resolution below 5 μm, an extremely low material budget (0.09% $X_0$ per layer), and a radiation tolerance up to 4×10$^{12}$ 1 MeV n$_{eq}$ cm$^{−2}$ for ALICE ITS3 detector.
Within the R&D framework, several pixel test structures have been designed and fabricated to explore both sensor geometries and front-end architectures. In particular, analog pixel test structures (APTS) featuring a fast operational amplifier (-OA) as output buffer have been developed to investigate the charge collection dynamics and the intrinsic timing performance of the sensor. Measurements performed with charge particle beam demonstrated a time resolution of 63 ps, combined with a charge collection efficiency above 99% and a spatial resolution better than 3 μm, with a stable performance up to 1×10$^{14}$ 1 MeV n$_{eq}$ cm$^{−2}$.
Building on these results, a second engineering run (ER2) introduced three novel APTS-OA variants targeting improved detector performance and radiation robustness. The new designs aim at enhancing the lateral electric field through sensor doping optimization, reducing the input capacitance from 2 fF to 600 aF. This upgrade will improve the signal-to-noise ratio, increasing radiation hardness while preserving fast charge collection. In addition, the front-end architecture has been optimized for small pixel pitches (10 μm), compatible with future ultra-high granularity tracking detectors.
This contribution presents the results obtained during a dedicated test beam campaign, with particular focus on charge collection properties and their impact on timing performance. A direct comparison among the ER2 variants and the baseline APTS-OA structure developed during the first TPSCo 65 nm technology validation phase will be discussed, highlighting the effect of sensor optimization on charge sharing, signal formation, and time resolution.