Static-Linearity Enhancement Techniques for Digital-to-Analog Converters Exploiting Optimal Arrangements of Unit Elements

Driven by the ongoing challenge of designing high-accuracy digital-to-analog converters (DACs) at the cost of a relatively small area occupation, optimal combination algorithms (OCAs) recently gained attention within the myriad of possible calibration techniques for DACs. OCAs show appealing properties with respect to traditional approaches such as dynamic element matching (DEM). At start-up or upon request, mismatches affecting DAC elements are measured on-chip, allowing rearrangement in the selection logic of the DAC unit elements. The newly found arrangement is, hence, used during normal operation, achieving superior linearity. As of today, several alternative OCAs have been proposed; however, designers willing to implement OCA-calibrated DACs are faced with unclear tradeoffs and insufficient design guidelines. In this work, we provide a detailed comparison of existing OCAs based on statistical behavioral simulations. Starting from this, we investigate the relationships between OCAs' performances and circuit-level design aspects. Specifically, OCAs' effectiveness in improving the static linearity is linked to the number of DAC bits and the accuracy of the auxiliary comparator required by every OCA. Unforeseen trends emerge, and new design considerations are suggested, fostering novel awareness on the subject of high-accuracy DAC designs enabled by OCA-based calibration techniques.