Speaker
Description
Resonant inelastic X-ray scattering (RIXS) imaging directly combines spatial and spectral information by targeting specific atoms within complex structures while remaining sensitive to multiple excitations simultaneously. To effectively measure the scattering spectra of many interesting materials a high spatial resolution in one dimension in the soft X-ray regime (<3 keV) on the detector is needed. Sensitivity in this energy range proves challenging for standard silicon detectors [1].
In collaboration with Fondazione Bruno Kessler (FBK) the Paul Scherrer Institute (PSI) has developed inverse low-gain avalanche diode (iLGAD) sensors optimized for soft X-rays. For RIXS applications, iLGAD sensors with a rectangular pixel geometry were developed. The sensor is bonded to a JUNGFRAU ASIC [2] with a 75 μm square pixel pitch, taking advantage of its low-noise readout. The rectangular sensor pixels are mapped onto this square grid resulting in pixels that are elongated in one direction and compressed in the other, giving an effective pixel pitch of 15–25 μm along one dimension. This gives a charge-integrating hybrid pixel detector laid out to detect small features in one dimension at low energies.
Charge-integrating detectors enable improved position reconstruction through interpolation techniques that exploit charge sharing between neighbouring pixels. Using an 𝜂-algorithm a sub-pixel resolution of around 3 μm have been demonstrated with standard silicon sensors [3]. Here, we apply the same approach within the AARE framework [4] to rectangular iLGAD pixel detectors in the soft X-ray range. We performed a focused beam scan at the PolLux beam line at the Swiss Light Source (SLS) at different pixel pitches and energies. We will present the results, demonstrating a spatial resolution down to 1.8 μm and discuss the effect of pixel geometry and photon energy.
Another approach being studied is the use of machine learning algorithms to determine the photon absorption position. We are planning to use data of the focused beam scan to conduct supervised learning, studying the charge patterns generated by different impact positions. The aim is to improve the spatial resolution compared to the 𝜂-algorithm. Preliminary investigations along these lines are ongoing, and the results, if available, will be presented.
This research was funded in whole or in part by the Swiss National Science Foundation (SNSF) [PZ00P2 223377]. For the purpose of open access, a CC BY public copyright licence is applied to any author accepted manuscript (AAM) version arising from this submission.