A data-dependent energy reduction algorithm for SAR ADC using self-adaptive window. (June 2020)

• Record Type: Journal Article
• Authors: Lu, Hao-wei; Yu, Xiao-Peng; Lu, Zheng-Hao; Yeo, Kiat-Seng; Chen, Jer-Ming
• Is Part Of: Microelectronics journal. Volume 100(2020)
• Publication Date: 2020-06
• Subjects: Successive approximation register(SAR) -- Analog-to-digital converter(ADC) -- Bit-trial saving -- Biomedical detection -- Self-adaptive algorithm; Microelectronics -- Periodicals; Microélectronique -- Périodiques; Microelectronics; Electronic journals; Journals - contents and abstracts; Periodicals; 621.3805
• DOI: 10.1016/j.mejo.2020.104754
• Languages: English
• ISSNs: 0959-8324

Abstract

Abstract: A new data-dependent energy reduction algorithm for successive approximation register (SAR) analog-to-digital convert (ADC) is presented in this paper. The proposed algorithm starts with a less significant bit (LSB) window with N-bit length, which is configurable depending on signal characteristics. By using less significant bit to more significant bit (L2M) successive extending (SE), the signal window is self-adaptive to cover the input signal within boundary. The proposed technique leads to less mean bit trials per sample, suggesting higher energy efficiency in many data-dependant ADC applications. Furthermore, this algorithm can be implemented based on conventional charge redistribution SAR ADC without any change in analog circuits. According to MATLAB simulation, the proposed technique is able to reduce mean bit trials effectively in biomedical signal detection applications. The simulation results show 37.9%, 32.3% and 18.2% less mean bit trials than using conventional SAR algorithm in processing electrocardiogram (ECG), electroencephalogram (EEG) and in electro-myography (EMG) signals respectively.