Semiconductor radiation sensor device and method of training an artificial neural network for signal processing

The present disclosure relates to a semiconductor radiation sensor device for characterizing X-ray and/or gamma-ray radiation comprising: a converter comprising a plurality of physically spaced semiconductor sensors configured to convert incident X-ray and/or gamma-ray photons into electron-hole pairs; an electric field generator configured to apply an electric field to the plurality of physically spaced semiconductor sensors, thereby creating signals representative of a movement of charge carriers in the physically spaced semiconductor sensors; a readout circuitry being configured to read out the signals from said plurality of semiconductor sensors; and a processing unit connected to said readout circuitry, said processing unit being configured to estimate an interaction time and a three-dimensional interaction position of an event in said converter by processing the signals read out from the plurality of semiconductor sensors, wherein the processing unit comprises an artificial neural network that has been trained to generate the estimated interaction time and the three-dimensional interaction position of the event based on a model of physical and geometrical properties of the plurality of semiconductor sensors and based on simulated time-varying charges on the plurality of semiconductor sensors. The disclosure further relates to a method of training an artificial neural network to generate a three-dimensional characterization of incoming single photons.