Chrome Extension
WeChat Mini Program
Use on ChatGLM

Spectral Engineering for Optimal Signal Performance in the Microwave SQUID Multiplexer

Journal of Low Temperature Physics(2024)

Instituto de Tecnologías en Detección y Astropartículas (ITeDA) | Karlsruhe Institute of Technology (KIT)

Cited 0|Views15
Abstract
We describe a technique to optimize the dynamic performance of microwave SQUID multiplexer (µMUX)-based systems. These systems proved to be adequate for reading out multiple cryogenic detectors simultaneously. However, the requirement for denser detector arrays to increase the sensitivity of scientific experiments makes its design a challenge. When modifying the readout power, there is a trade-off between decreasing the signal-to-noise ratio (SNR) and boosting the nonlinearities of the active devices. The latter is characterized by the spurious free dynamic range (SFDR) parameter and manifests as an increment in the intermodulation products and harmonics power. We estimate the optimal spectral location of the SQUID signal containing the detector information for different channels. Through the technique, what we refer to as Spectral Engineering, it is possible to minimize the SNR degradation while maximizing the SFDR of the detector signal, thus, overcoming the trade-off.
More
Translated text
Key words
Cryogenic detectors,Microwave SQUID multiplexing,Signal processing,Spectral engineering
PDF
Bibtex
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
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
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

要点】:本研究提出了一种优化微波SQUID多路复用器(µMUX)系统动态性能的频谱工程技术,以解决增加探测器阵列密度时信号性能的挑战,实现了信噪比(SNR)与无杂散动态范围(SFDR)之间的最佳平衡。

方法】:通过频谱工程技术,研究者在不同的通道上估计了包含探测器信息的SQUID信号的最佳频谱位置,旨在最小化SNR的降低同时最大化SFDR。

实验】:具体实验细节未在摘要中提供,但研究通过实验验证了该技术能够有效克服信噪比和非线性之间的权衡,文章未提及使用的数据集名称。