Performance Prediction of Incremental ΔΣ ADCs

Abstract

incremental Delta-Sigma (I-DS) analog-to-digital converters (ADCs) are widely utilized in applications requiring high-resolution Nyquist conversion. Accurate performance prediction of these converters is crucial for efficient design and optimization. Existing state of the art (SoA) equations are either lacking sufficient accuracy or simplicity in predicting quantization noise performance under various architectural scenarios. This paper reviews the derivation and limitations of existing performance prediction models. A more general and accurate analysis is derived for predicting the performance of I-DS ADCs, addressing the shortcomings of the conventional approaches. The validity of the proposed performance prediction is rigorously evaluated through extensive simulations across a broad range of architectural choices. The results establish the new model as a robust tool for predicting the performance of I-DS ADCs, advancing the SoA, and facilitating more effective design strategies in the field.