Speaker
Description
Neutron detection has historically been achieved using
Several aspects are cited in the literature as limiting factors of current 3D neutron detectors: (i) non-optimized micro-structure geometry (diameter/width, depth, and pitch), (ii) inactive layers at the silicon/converter interface, and (iii) difficult conformal deposition of the neutron converters.
Our project, "INDET – Improved efficiency for Neutron DETectors", aims at addressing several of these limitations.
The sensors were designed and fabricated at SINTEF MiNaLab, using a N-on-P planar process with a shallow entrance window and p-spray isolation. The wafer layout includes diodes, strip detectors and pixel detectors compatible with the Medipix/TimePix readout chips. The micro-structures housing the neutron converter are etched by means of Deep Reactive Ion Etching - DRIE.
The 3D microstructures are not doped to reduce the extension of the dead layers that could negatively affect the detection of the conversion products. Passivation of the structures is achieved by Atomic Layer Deposition (ALD) of Al
The neutron converter of choice is
The final 3D micro-structures implemented on the sensors were designed with the aid of Geant4 simulations using the NCrystal library [8]. Simulations were carried out at the University of Bergen. Different combinations of structure shape, size, depth, and pitch were investigated to study their effect on neutron conversion probability and detection efficiency. The peak simulated efficiency was found to be 31.8% for trenches of width
In this presentation, we will report on the details of the design aspects, microstructure geometry and their fabrication/passivation, neutron converter depositions and electrical measurements. The expected neutron detection efficiency will be discussed. In-depth characterisation of the sensors in neutron beams will be carried out at different facilities. The first results are expected before the end of 2022.
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The authors acknowledge funding from the Norwegian Research Council (Research Project no. 289437 – NANO2021 program).