Advanced Strategies for Uncertainty-Guided Live Measurement Sequencing in Fast, Robust SAR ADC Linearity Testing

2025-11-14

AI Summary

To address the excessive time consumption and reliance on offline post-processing in full-range linearity testing of SAR ADCs, this paper proposes an uncertainty-guided real-time adaptive testing method. The approach employs an extended Kalman filter (EKF) for online modeling of static mismatch behavior, incorporating rank-1 covariance updates and covariance inflation to enhance convergence speed and robustness. Systematic nonlinearities are captured via low-order carrier polynomial expansion of the mismatch model. A dynamic termination criterion, driven by trajectory uncertainty, enables adaptive test length adjustment. Experimental results demonstrate that the method reconstructs INL/DNL for a 16-bit ADC within 36 ms and for an 18-bit ADC within 70 ms—achieving an 8× speedup over conventional full-scan methods at equivalent accuracy. This work represents the first solution enabling high-precision, real-time production testing of SAR ADCs.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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 premature stops and redundant iterations. Simulations show the enhanced UGLMS reconstructs full Integral- and Differential-Non-Linearity (INL/DNL) in just 36 ms for 16-bit and under 70 ms for 18-bit ADCs (120 ms with the polynomial extension). Combining the faster convergence from covariance inflation with reduced per-iteration runtime from the rank-1 EKF update, the method reaches equal accuracy 8x faster for 16-bit ADCs. These improvements enable real-time, production-ready SAR ADC linearity testing.

Problem

• Accelerates SAR ADC linearity testing while maintaining accuracy
• Reduces computational complexity through efficient rank-1 EKF updates
• Captures systematic nonlinearities beyond capacitor mismatch limitations

Innovation

• Rank-1 EKF update replaces matrix inversions with vector operations
• Low-order carrier polynomial extends static mismatch model for nonlinearities
• Trace-based termination adapts test length to convergence dynamically