Uncertainty-Guided Live Measurement Sequencing for Fast SAR ADC Linearity Testing

TL;DR

This paper presents an adaptive, real-time measurement sequencing method for fast linearity testing of high-resolution SAR ADCs, significantly reducing test time and complexity.

Contribution

It introduces an uncertainty-guided, EKF-based adaptive measurement approach that eliminates large data collection and post-processing in ADC linearity testing.

Findings

Reduces total test time compared to traditional methods

Provides immediate feedback for measurement targeting

Achieves accurate capacitor mismatch estimation in real time

Abstract

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.