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

2025-11-14

AI Summary

To address the inefficiency of conventional high-resolution SAR ADC linearity testing—reliant on dense sampling and offline post-processing—this paper proposes a real-time closed-loop adaptive testing methodology. Central to the approach is uncertainty-guided measurement sequence planning, integrated with extended Kalman filtering (EKF) for online modeling and parameter estimation; measurement points are dynamically selected to maximize information gain, thereby drastically reducing redundant sampling. The method enables rapid, accurate identification of non-ideal parameters—such as capacitor mismatch—without extensive data acquisition or offline computation. Experimental results demonstrate that, compared to traditional histogram-based and sine-fitting methods, the proposed technique reduces test time by over an order of magnitude and significantly lowers computational overhead, while exhibiting strong robustness and compatibility with high-volume production integration.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements

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.

Problem

• Reduces test time for high-resolution SAR ADC linearity testing
• Eliminates need for dense data acquisition and offline processing
• Dynamically selects measurements using real-time uncertainty guidance

Innovation

• Uses Extended Kalman Filter for real-time model refinement
• Dynamically selects measurement points based on uncertainty
• Eliminates need for large-scale data collection