Objective: A new technique is presented to enhance the precision of the analog-to-digital (AD) and digital-to-analog (DA) conversion, which are fundamental operations of many biomedical information processing systems. In practice, the precision of these operations is always bounded, first by the random mismatch error occurred during system implementation, and subsequently by the intrinsic quantization error determined by the system architecture itself. Methods: Here, we derive a new mathematical interpretation of the previously proposed redundant sensing architecture that not only suppresses mismatch error but also allows achieving an effective resolution exceeding the system's intrinsic resolution, i.e., super-resolution (SR). SR is enabled by an endogenous property of redundant structures regarded as 'code diffusion' where the references' value spreads into the neighbor sample space as a result of mismatch error. Results: Using Monte Carlo methods, we show a profound theoretical increase of 8-9 b effective resolution or 256-512× enhancement of precision on a 10-b device at 95% sample space. Conclusion: The proposed SR mechanism can be applied to substantially improve the precision of various AD and DA conversion processes beyond the system resource constraints. Significance: The concept opens the possibility for a wide range of applications in low-power fully integrated sensors and devices where the cost-accuracy tradeoff is inevitable. As a proof-of-concept demonstration, we point out an example where the proposed technique can be used to enhance the precision of an implantable neurostimulator design.
Bibliographical noteFunding Information:
Manuscript received May 29, 2018; revised October 9, 2018 and December 3, 2018; accepted December 4, 2018. Date of publication December 7, 2018; date of current version July 17, 2019. This work was supported in part by the Defense Advanced Research Projects Agency, Biological Technologies Office, under Contract HR0011-17-2-0060, and in part by the internal funding from the University of Minnesota. (Diu Khue Luu and Anh Tuan Nguyen are co-first authors.) (Corresponding author: Zhi Yang.) D. K. Luu and A. T. Nguyen are with the Department of Biomedical Engineering, University of Minnesota.
- Analog-to-digital converter
- Digital-to-analog converter
- Low-power sensors and devices
- Mismatch error
- Quantization error
- Redundant sensing
PubMed: MeSH publication types
- Journal Article
- Research Support, Non-U.S. Gov't
- Research Support, U.S. Gov't, Non-P.H.S.