Comparative Study and Review on Successive Approximation/Stochastic Approximation Analog to Digital Converters for Biomedical Applications

Abstract

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.