AISAD: A User-Friedly AI-Assisted MATLAB Tool for the High-Level Design of ΣΔ Modulators

This paper presents a toolbox for the optimization and automated design of Analog-to-Digital Converters (ADCs). The tool combines Artificial Neural Networks (ANNs) and behavioral simulation for the optimized high-level sizing process, i.e. to map system-level specifications into building-block requirements. To this end, ANNs are trained to identify the best ADC architecture for a given set of specifications as well as the optimum set of design parameters which yields these specifications. Designs produced by the ANNs are iterated with time-domain behavioral simulation in order to find the best figure of merit. The presented toolbox – applied to Sigma-Delta Modulators ($\Sigma \Delta \mathrm{Ms}$) – includes a Graphical User Interface (GUI) implemented in MATLAB, which guides the user through all steps: from definition of specifications to verification of the obtained results<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.