arXiv subjects

Marta Tornago

Publications and source records attributed to Marta Tornago.

4 recordsLinked to original sources

arXiv2022

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

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 $μm$ for 200-$μm$ 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.

physics.ins-det↗

arXiv2022

A Compensated Design of the LGAD Gain Layer

In this contribution, we present an innovative design of the Low-Gain Avalanche Diode (LGAD) gain layer, the p$^+$ implant responsible for the local and controlled signal multiplication. In the standard LGAD design, the gain layer is obtained by implanting $\sim$ 5E16/cm$^3$ atoms of an acceptor material, typically Boron or Gallium, in the region below the n$^{++}$ electrode. In our design, we aim at designing a gain layer resulting from the overlap of a p$^+$ and an n$^+$ implants: the difference between acceptor and donor doping will result in an effective concentration of about 5E16/cm$^3$, similar to standard LGADs. At present, the gain mechanism of LGAD sensors under irradiation is maintained up to a fluence of $\sim$ 1-2E15/cm$^2$, and then it is lost due to the acceptor removal mechanism. The new design will be more resilient to radiation, as both acceptor and donor atoms will undergo removal with irradiation, but their difference will maintain constant. The compensated design will empower the 4D tracking ability typical of the LGAD sensors well above 1E16/cm$^2$.

physics.ins-det↗

arXiv2022

Optimization of the Gain Layer Design of Ultra-Fast Silicon Detectors

In the past few years, the need of measuring accurately the spatial and temporal coordinates of the particles generated in high-energy physics experiments has spurred a strong R\&D in the field of silicon sensors. Within these research activities, the so-called Ultra-Fast Silicon Detectors (UFSDs), silicon sensors optimized for timing based on the Low-Gain Avalanche Diode (LGAD) design, have been proposed and adopted by the CMS and ATLAS collaborations for their respective timing layers. The defining feature of the Ultra-Fast Silicon Detectors (UFSDs) is the internal multiplication mechanism, determined by the gain layer design. In this paper, the performances of several types of gain layers, measured with a telescope instrumented with a $^{90}$Sr $β$-source, are reported and compared. The measured sensors are produced by Fondazione Bruno Kessler (FBK) and Hamamatsu Photonics (HPK). The sensor yielding the best performance, both when new and irradiated, is an FBK 45\mum-thick sensor with a carbonated deep gain implant, where the carbon and the boron implants are annealed concurrently with a low thermal load. This sensor is able to achieve a time resolution of 40~ps up to a radiation fluence of~\fluence{2.5}{15}, delivering at least 5~fC of charge.

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arXiv2021

First application of machine learning algorithms to the position reconstruction in Resistive Silicon Detectors

RSDs (Resistive AC-Coupled Silicon Detectors) are n-in-p silicon sensors based on the LGAD (Low-Gain Avalanche Diode) technology, featuring a continuous gain layer over the whole sensor area. The truly innovative feature of these sensors is that the signal induced by an ionising particle is seen on several pixels, allowing the use of reconstruction techniques that combine the information from many read-out channels. In this contribution, the first application of a machine learning technique to RSD devices is presented. The spatial resolution of this technique is compared to that obtained with the standard RSD reconstruction methods that use analytical descriptions of the signal sharing mechanism. A Multi-Output regressor algorithm, trained with a combination of simulated and real data, leads to a spatial resolution of less than 2 $μm$ for a sensor with a 100 $μm$ pixel. The prospects of future improvements are also discussed.

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