In this paper, we describe DECAL, a prototype Monolithic Active Pixel Sensor (MAPS) device designed to demonstrate the feasibility of both digital calorimetry and reconfigurability in ASICs for particle physics. The goal of this architecture is to help reduce the development and manufacturing costs of detectors for future colliders by developing a chip that can operate both as a digital silicon calorimeter and a tracking chip. The prototype sensor consists of a matrix of 64 × 64 55 (Formula presented.) m pixels, and provides a readout at 40 MHz of the number of particles which have struck the matrix in the preceding 25 ns. It can be configured to report this as a total sum across the sensor (equivalent to the pad of an analogue calorimeter) or the sum per column (equivalent to a traditional strip detector). The design and operation of the sensor are described, and the results of chip characterisation are reported and compared to simulations.

DECAL: A Reconfigurable Monolithic Active Pixel Sensor for Tracking and Calorimetry in a 180 nm Image Sensor Process

Laura Gonella;
2022-01-01

Abstract

In this paper, we describe DECAL, a prototype Monolithic Active Pixel Sensor (MAPS) device designed to demonstrate the feasibility of both digital calorimetry and reconfigurability in ASICs for particle physics. The goal of this architecture is to help reduce the development and manufacturing costs of detectors for future colliders by developing a chip that can operate both as a digital silicon calorimeter and a tracking chip. The prototype sensor consists of a matrix of 64 × 64 55 (Formula presented.) m pixels, and provides a readout at 40 MHz of the number of particles which have struck the matrix in the preceding 25 ns. It can be configured to report this as a total sum across the sensor (equivalent to the pad of an analogue calorimeter) or the sum per column (equivalent to a traditional strip detector). The design and operation of the sensor are described, and the results of chip characterisation are reported and compared to simulations.
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3100906
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