Advanced Strategies for Uncertainty-Guided Live Measurement Sequencing in Fast, Robust SAR ADC Linearity Testing
Thorben Schey, Khaled Karoonlatifi, Michael Weyrich, Andrey Morozov

TL;DR
This paper presents an improved uncertainty-guided live measurement sequencing method for fast, real-time SAR ADC linearity testing, combining algorithmic enhancements and model extensions to significantly reduce test runtime.
Contribution
The paper introduces an enhanced UGLMS method with a rank-1 EKF update, covariance-inflation, and polynomial mismatch modeling, enabling faster and more accurate ADC linearity testing.
Findings
Reconstructs INL/DNL in 36 ms for 16-bit ADCs
Achieves 8x speedup over previous methods for 16-bit ADCs
Maintains accuracy with reduced test runtimes
Abstract
This paper builds on our Uncertainty-Guided Live Measurement Sequencing (UGLMS) method. UGLMS is a closed-loop test strategy that adaptively selects SAR ADC code edges based on model uncertainty and refines a behavioral mismatch model in real time via an Extended Kalman Filter (EKF), eliminating full-range sweeps and offline post-processing. We introduce an enhanced UGLMS that delivers significantly faster test runtimes while maintaining estimation accuracy. First, a rank-1 EKF update replaces costly matrix inversions with efficient vector operations, and a measurement-aligned covariance-inflation strategy accelerates convergence under unexpected innovations. Second, we extend the static mismatch model with a low-order carrier polynomial to capture systematic nonlinearities beyond pure capacitor mismatch. Third, a trace-based termination adapts test length to convergence, preventing…
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