ISSN 1004-4140
CN 11-3017/P

多电压阈值数字化方法

Multi-Voltage Threshold Digitization Method

  • 摘要: 模数转换器将物理量随时间变化的模拟信号转换为易于储存和处理的数字信号,是自然科学和信息科学的桥梁,也是现代工业与科研中不可或缺的核心基础器件。20世纪20年代以来,随着均匀时域采样的电子学实现和数学理论陆续建立,均匀时域采样成为模数转换领域的基石。此后数十年,正电子发射断层成像、核聚变中子能谱和中微子探测等需要对大量通道高速信号进行采样的应用不断涌现。在这些应用中,基于均匀时域采样的模数转换方案存在功耗大、成本高等缺点,以至于不得不在数字化前进行预处理,从而损失了信号的原始信息。基于值域采样的多电压阈值数字化方法通过若干电压阈值对信号进行采样,再在后端结合先验信息重建信号,使得大量通道高速信号的精确数字化成为可能。目前,多电压阈值数字化方法已被运用在正电子发射断层成像、X射线安检、中子石油测井和质子治疗监测等领域中。本文概述多电压阈值数字化方法的原理,介绍近年来多电压阈值数字化电子学的研究进展,展望多电压阈值数字化方法领域的研究趋势。

     

    Abstract: Analog-to-digital converters (ADCs) transform the analog signals of time-variant physical quantity into digital signals for storing and processing. As the bridge between natural science and information science, ADCs are indispensable in modern industry and scientific research. Since the 1920s, uniform time-domain sampling has become the basic principle in the field of ADCs with the establishment of its electronic implementation and mathematical theory. In the following decades, applications such as positron emission tomography (PET), nuclear fusion neutron spectrum, neutrino detection, etc., which require sampling of many high-speed signals, have emerged one after another. In these applications, ADCs based on uniform time-domain sampling show disadvantages of high power consumption and high cost, so the signal has to be pre-processed before digitalization with losing the original information of the signal. Based on value-domain sampling, the Multi-Voltage Threshold (MVT) method digitizes the signal through several voltage thresholds and then reconstructs the signal with a computer using prior information. The MVT method makes it possible to accurately digitize a large number of high-speed signals. At present, the MVT method has been applied in PET, X-ray security inspection, neutron logging, proton therapy monitoring, etc. This paper outlines the principle of the MVT method, introduces the research progress of MVT electronics in recent years, and further provides an outlook of the MVT research trend.

     

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