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Marco Mandurrino

Publications and source records attributed to Marco Mandurrino.

6 recordsLinked to original sources

Realistic Monte Carlo simulations of silicon 4D-trackers

Simulation-guided design represents a fundamental contribution towards the development of modern semiconductor devices aiming to reach high-performance particle detection, identification and tracking, and constitutes a strategic element of the new detector R&D roadmap. At the same time, the complexity of microelectronic structures and the related detection systems is drastically increasing, also thanks to the progressive scaling down of the design rules with the process technology. Owing to the capability to embed a detailed description of the ionization mechanism into a device-level framework, as well as capture the stochastic nature of signal formation, the Monte Carlo (MC) approach has become the most recommended strategy to achieve reliable predictions of the dynamic properties of particle detectors in realistic settings such as in-beam experiments. This work gives an overview of the key aspects characterizing MC tools, with particular emphasis on the Garfield++ simulation toolkit. To this end, the analysis of some specific case studies related to the design of silicon particle detectors for timing and 4D-tracking in both current and future high-energy physics experiments will be presented, showing the comparison of measured and simulated figures-of-merit and highlighting strengths and open challenges of this approach. The examples are intentionally chosen from the family of Monolithic Active Pixel Sensors, as they represent some of the most promising and relevant advancements in particle detection, and because the CMOS monolithic integration offers the most versatile platform for testing the robustness of numerical designs.

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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.

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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$^2$.

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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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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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Silicon detectors for the LHC Phase-II upgrade and beyond. RD50 status report

It is foreseen to significantly increase the luminosity of the LHC in order to harvest the maximum physics potential. Especially the Phase-II-Upgrade foreseen for 2023 will mean unprecedented radiation levels, significantly beyond the limits of the Silicon trackers currently employed. All-Silicon central trackers are being studied in ATLAS, CMS and LHCb, with extremely radiation-hard Silicon sensors to be employed on the innermost layers. Within the RD50 Collaboration, a large R&D program has been underway for more than a decade across experimental boundaries to develop Silicon sensors with sufficient radiation tolerance for HL-LHC trackers. Key areas of recent RD50 research include new sensor fabrication technologies such as HV-CMOS, exploiting the wide availability of the CMOS process in the semiconductor industry at very competitive prices compared to the highly specialized foundries that normally produce particle detectors on small wafers. We also seek for a deeper understanding of the connection between the macroscopic sensor properties such as radiation-induced increase of leakage current, doping concentration and trapping, and the microscopic properties at the defect level. Another strong activity is the development of advanced sensor types like 3D Silicon detectors, designed for the extreme radiation levels expected for the vertexing layers at the HL-LHC. A further focus area is the field of LGADs, where a dedicated multiplication layer to create a high field region is built into the sensor. LGADs are characterized by a high signal also after irradiation and a very fast signal compared to traditional Silicon detectors with make them ideal candidates for ATLAS and CMS timing layers in the HL-LHC. We will present the state of the art in several Silicon detector technologies as outlined above and at radiation levels corresponding to HL-LHC fluences and partially beyond.

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