Machine Learning Based Calibration Techniques for ADCs: An Overview

Analog-to-digital converters (ADCs) are essential components in modern signal processing systems, but their performance is often constrained by non-idealities such as mismatches, gain errors, offsets, etc. Two most popular ADC toplogies, viz., Successive Approximation Register (SAR) ADC whose performance is mostly affected by capacitor mismatch and Pipelined ADCs whose performance is mostly affected by capacitor mismatch and gain errors are discussed in this paper. Machine learning (ML)-based calibration techniques have recently emerged as effective tools to mitigate these challenges and enhance ADC performance. This paper provides a comprehensive overview of ML-driven approaches for calibrating SAR and Pipelined ADCs, emphasizing key methodologies, advantages, and limitations. Additionally, traditional Least Mean Squares (LMS)-based calibration methods are discussed and shown to be a limiting case of ML-based calibration.