Predictive sensing in analog-to-digital converters for biomedical applications

Abstract

This paper presents a predictive sensing-based ADC architecture that has improved energy efficiency, compared to a conventional SAR ADC, by exploiting the predictable properties of biomedical signals, such as the electrocardiogram (ECG) signal. By predicting the next input sample, based on previous samples, the conversion is performed in a subrange of the full scale. This results in energy savings compared to the SAR ADC, which always performs the conversion in the full scale. Two search algorithms to perform the conversion in the subrange will be presented and analyzed. For moderate resolutions between 6 and 10 bit, up to 40–50% improvement in terms of energy consumption is obtained, while 25 to 40% for higher resolutions. To validate the concept, a 12-bit predictive ADC, implementing the restricted binary search algorithm with 0-th order prediction, is designed and simulated in 130nm UMC CMOS technology. The simulation results show an improvement in the average energy consumption per conversion, compared to a conventional SAR ADC with the same resolution, which is in the range of 30–40%.