Silicon sensors with resistive read-out: Machine Learning techniques for ultimate spatial resolution

TL;DR

This paper presents resistive AC-coupled Silicon Detectors with innovative electrode design achieving high spatial resolution, utilizing machine learning algorithms for precise position determination across the pixel surface.

Contribution

Introduction of RSD2, a novel resistive silicon detector with optimized electrode design, combined with machine learning techniques to enhance spatial resolution.

Findings

Achieved 8 μm spatial resolution over the entire pixel surface.

Demonstrated effective use of machine learning algorithms for position reconstruction.

Validated detector performance with Transient Current Technique measurements.

Abstract

Resistive AC-coupled Silicon Detectors (RSDs) are based on the Low Gain Avalanche Diode (LGAD) technology, characterized by a continuous gain layer, and by the innovative introduction of resistive read-out. Thanks to a novel electrode design aimed at maximizing signal sharing, RSD2, the second RSD production by Fondazione Bruno Kessler (FBK), achieves a position resolution on the whole pixel surface of about 8 $\mu m$ for 200-$\mu m$ pitch. RSD2 arrays have been tested using a Transient Current Technique setup equipped with a 16-channel digitizer, and results on spatial resolution have been obtained with machine learning algorithms.