生物医学应用中连续逼近/随机逼近模数转换器的比较研究与综述

G. Snehalatha, J. Selvakumar, Esther Rani Thuraka
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引用次数: 0

摘要

利用CMOS技术实现的数据转换器在日益增长的电子领域发挥着至关重要的作用。adc在信号处理和通信应用中得到了广泛的应用。由于小面积、低功耗和低/中输入信号,连续逼近adc在大多数应用中是首选。机器学习算法用于微调连续随机逼近模拟数字转换器(SSA ADC),该转换器用于生物医学应用。与SAR ADC相比,SSA ADC功耗低,并且可以使用随机过程最大程度地纠正DAC引起的误差。生物医学应用的各种ADC、SAR ADC和SSA ADC架构在参数、方法和工具方面进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative Study and Review on Successive Approximation/Stochastic Approximation Analog to Digital Converters for Biomedical Applications
Data converters implemented using CMOS technology play crucial role in electronics which is ever increasing. ADCs find their applications in signal processing and communication applications. Because of small area, low power and low/medium input signals Successive Approximation ADCs are preferred in most of the applications. Machine Learning algorithms are used to fine-tune the Successive Stochastic Approximation Analog to Digital Converter (SSA ADC), which is used in Biomedical applications. Compared to SAR ADC, SSA ADC offers low power and errors caused by DAC can be corrected to maximum possible extent using stochastic process. Various ADCs, SAR ADC and SSA ADC architectures for Biomedical applications have been compared with respect to parameters, methods and tools.
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