Uncertainty-Guided Live Measurement Sequencing for Fast SAR ADC Linearity Testing
For engineers testing high-resolution SAR ADCs in production, this method reduces test time and complexity, but the improvements are incremental over existing adaptive approaches.
This paper introduces an adaptive closed-loop testing methodology for SAR ADC linearity testing that uses an Extended Kalman Filter to estimate capacitor mismatch parameters in real time, dynamically selecting measurement points based on model uncertainty. Experimental results show substantial reductions in test time and computational overhead compared to existing methods.
This paper introduces a novel closed-loop testing methodology for efficient linearity testing of high-resolution Successive Approximation Register (SAR) Analog-to-Digital Converters (ADCs). Existing test strategies, including histogram-based approaches, sine wave testing, and model-driven reconstruction, often rely on dense data acquisition followed by offline post-processing, which increases overall test time and complexity. To overcome these limitations, we propose an adaptive approach that utilizes an iterative behavioral model refined by an Extended Kalman Filter (EKF) in real time, enabling direct estimation of capacitor mismatch parameters that determine INL behavior. Our algorithm dynamically selects measurement points based on current model uncertainty, maximizing information gain with respect to parameter confidence and narrowing sampling intervals as estimation progresses. By providing immediate feedback and adaptive targeting, the proposed method eliminates the need for large-scale data collection and post-measurement analysis. Experimental results demonstrate substantial reductions in total test time and computational overhead, highlighting the method's suitability for integration in production environments.