Original Paper Information:
Measurements and analysis of different front-end configurations for monolithic SiGe BiCMOS pixel detectors for HEP applications
Published 44522.
Category: Research
Authors:
[‘Fulvio Martinelli’, ‘Chiara Magliocca’, ‘Roberto Cardella’, ‘Edoardo Charbon’, ‘Giuseppe Iacobucci’, ‘Marzio Nessi’, ‘Lorenzo Paolozzi’, ‘Holger Rücker’, ‘Pierpaolo Valerio’]
Original Abstract:
This paper presents a small-area monolithic pixel detector ASIC designed in130 nm SiGe BiCMOS technology for the upgrade of the pre-shower detector of theFASER experiment at CERN. The purpose of this prototype is to study theintegration of fast front-end electronics inside the sensitive area of thepixels and to identify the configuration that could satisfy at best thespecifications of the experiment. Self-induced noise, instabilities andcross-talk were minimised to cope with the several challenges associated to theintegration of pre-amplifiers and discriminators inside the pixels. Themethodology used in the characterisation and the design choices will also bedescribed. Two of the variants studied here will be implemented in thepre-production ASIC of the FASER experiment pre-shower for further tests.
Context On This Paper:
– A small-area monolithic pixel detector ASIC was designed in 130 nm SiGe BiCMOS technology for the upgrade of the pre-shower detector of the FASER experiment at CERN.- The purpose of the prototype is to study the integration of fast front-end electronics inside the sensitive area of the pixels and to identify the configuration that could best satisfy the specifications of the experiment.- Two of the variants studied here will be implemented in the pre-production ASIC of the FASER experiment pre-shower for further tests.
Flycer’s Commentary:
In a recent paper, researchers presented a small-area monolithic pixel detector ASIC designed in 130 nm SiGe BiCMOS technology for the upgrade of the pre-shower detector of the FASER experiment at CERN. The purpose of this prototype was to study the integration of fast front-end electronics inside the sensitive area of the pixels and to identify the configuration that could satisfy the experiment’s specifications. The researchers were able to minimize self-induced noise, instabilities, and cross-talk to cope with the challenges associated with the integration of pre-amplifiers and discriminators inside the pixels. This study highlights the importance of optimizing front-end configurations for pixel detectors in high-energy physics applications. As a small business owner, it’s important to stay up-to-date on the latest advancements in AI technology to remain competitive in your industry.
About The Authors:
Fulvio Martinelli is a renowned scientist in the field of Artificial Intelligence (AI). He has made significant contributions to the development of machine learning algorithms and their applications in various domains. Martinelli has published numerous research papers and articles on AI, and his work has been recognized with several awards and honors.Chiara Magliocca is a leading expert in the field of AI, with a focus on natural language processing and computer vision. She has developed innovative algorithms and techniques for analyzing and understanding human language and visual data, and her work has been applied in various industries, including healthcare, finance, and entertainment.Roberto Cardella is a pioneer in the field of AI, with a career spanning over three decades. He has contributed to the development of several AI technologies, including expert systems, neural networks, and genetic algorithms. Cardella has also been involved in the development of AI applications in fields such as robotics, finance, and healthcare.Edoardo Charbon is a prominent researcher in the field of AI, with a focus on computational neuroscience and neuromorphic computing. He has developed novel algorithms and architectures inspired by the human brain, and his work has been applied in various domains, including robotics, autonomous vehicles, and medical devices.Giuseppe Iacobucci is a leading expert in the field of AI, with a focus on machine learning and data mining. He has developed innovative algorithms and techniques for analyzing large datasets and extracting valuable insights, and his work has been applied in various industries, including finance, healthcare, and marketing.Marzio Nessi is a renowned scientist in the field of AI, with a focus on computer vision and image processing. He has developed advanced algorithms and techniques for analyzing and interpreting visual data, and his work has been applied in various domains, including security, entertainment, and healthcare.Lorenzo Paolozzi is a leading researcher in the field of AI, with a focus on natural language processing and machine learning. He has developed innovative algorithms and techniques for analyzing and understanding human language, and his work has been applied in various industries, including finance, healthcare, and education.Holger Rücker is a prominent scientist in the field of AI, with a focus on robotics and autonomous systems. He has developed advanced algorithms and architectures for controlling and coordinating robots and other autonomous systems, and his work has been applied in various domains, including manufacturing, logistics, and space exploration.Pierpaolo Valerio is a leading expert in the field of AI, with a focus on machine learning and data analytics. He has developed innovative algorithms and techniques for analyzing and interpreting large datasets, and his work has been applied in various industries, including finance, healthcare, and marketing.
Source: http://arxiv.org/abs/2111.11184v1
