Quad-Module Characterization with the MALTA Monolithic Pixel Chip
Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment(2024)
CERN
Abstract
The MALTA silicon pixel detector combines a depleted monolithic active pixel sensor (DMAPS) with a fully asynchronous front-end and readout. It features a high granularity pixel matrix with a 36.4 μm symmetric pixel pitch, low power consumption of <1μW/pixel and low material budget with detector thicknesses as little as 50 μm. It achieves a radiation hardness to 100MRad TID and more than 1×10E15 1 MeV neq/cm2 with a time resolution of <2 ns (Pernegger et al., 2023).In order to cover large sensitive areas efficiently with a minimum of power and data connections the development of modules, comprising of up to 4 MALTA detectors, is studied.This contribution presents the beam test performance of parallel and serial powered MALTA 4-chip modules in an effort to characterize the sensor’s chip-to-chip data and power transmission and prepare the production of a first prototype of an ultra-light weight 4-chip module on a flexible circuit with next generation MALTA2 sensors.
MoreTranslated text
Key words
MALTA,Silicon pixel detector,Monolithic,Large-area,Radiation hard,Module
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper