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Simone Emiliani

Publications and source records attributed to Simone Emiliani.

3 recordsLinked to original sources

Soft X-Ray Detection Down to the Nitrogen K-Edge Using a Pixelated iLGAD Sensor

Hybrid pixel detectors represent the state-of-the-art technology for hard X-ray experiments at synchrotron light sources and free-electron lasers, owing to their high frame rates, extended dynamic range, and scalable detection areas. However, their application in the soft X-ray regime remains bottlenecked by the challenge of developing a sensor that can achieve high quantum efficiency and an sufficient signal-to-noise ratio at low photon energies. Here, we present inverse low-gain avalanche diode (iLGAD) sensors integrated with a thin entrance window, demonstrating single-photon resolution down to 390 eV (below the Nitrogen K-edge) with a signal-to-noise ratio of 5. Two gain layer designs, standard gain layer and shallow gain layer, were systematically investigated to evaluate their spectral response as a function of photon energy and operating temperature. A double-peak feature is observed in the energy spectrum, arising from the dependence of charge multiplication on photon absorption depth. Furthermore, a phenomenological spectral fitting model was established, enabling the quantitative extraction of the effective $\mathrm{n}^+$ layer thickness and gain layer depth. Beyond providing a fundamental understanding of iLGAD charge multiplication mechanisms and characterization results in the soft X-ray regime, these findings lay the foundation for developing large-area, high-frame-rate hybrid pixel detectors for soft X-ray. This advancement directly benefits soft X-ray photon science applications, including high-resolution ptychography, spectroscopy, and resonant scattering at synchrotron and FEL beamlines.

physics.ins-det↗

Enhanced micrometre spatial resolution and usable photon flux for hybrid pixel detectors via deep-learning-based X-ray localization

Micrometre spatial resolution is achieved for X-rays with the MÖNCH detector, a charge-integrating hybrid pixel detector with a 25 μm pixel pitch, by exploiting charge sharing between neighbouring pixels. The conventional analytical interpolation method, however, relies on a global mapping derived from the charge-sharing statistics and is applicable only to isolated single-photon clusters, which limits both the achievable resolution and the usable photon flux. To overcome these limitations, we trained deep learning models to localize single-photon and pile-up events based on high-fidelity simulation data. For knife-edge measurements at 12 keV and a low occupancy of 0.74% (photons per pixel and frame), where single-photon events dominate, the proposed method reaches a of the edge spread function of 1.36 μm, compared with 1.54 μm for the conventional eta interpolation. We demonstrate experimentally that resolving up to three photons per cluster raises the reconstruction efficiency from 59% to 93% at a 2.4{\times} higher occupancy of 1.81%, while preserving a of 1.39 μm.

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Characterisation of the first wafer-scale prototype for the ALICE ITS3 upgrade: the monolithic stitched sensor (MOSS)

This paper presents the characterisation and testing of the first wafer-scale monolithic stitched sensor (MOSS) prototype developed for the ALICE ITS3 upgrade that is to be installed during the LHC Long Shutdown 3 (2026-2030). The MOSS chip design is driven by the truly cylindrical detector geometry that imposes that each layer is built out of two wafer-sized, bent silicon chips. The stitching technique is employed to fabricate sensors with dimensions of 1.4 $\times$ 25.9 cm, thinned to 50 $μ$m. The chip architecture, in-pixel front-end, laboratory and in-beam characterisation, susceptibility to single-event effects, and series testing are discussed. The testing campaign validates the design of a wafer-scale stitched sensor and the performance of the pixel matrix to be within the ITS3 requirements. The MOSS chip demonstrates the feasibility of the ITS3 detector concept and provides insights for further optimisation and development.

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