Optimized Design of ΣΔ Modulators Using Deep‐Learning and Simulated Annealing

ABSTRACT This letter presents a MATLAB toolbox for the automated high‐level design and optimization of analogue‐to‐digital converters (ADCs), using sigma‐delta modulators () as case studies. The tool combines machine learning (ML) techniques and behavioural simulation to obtain the optimum set of building‐block (amplifiers, comparators, etc.) requirements for a given set of specifications, namely resolution, signal bandwidth and power consumption. Two machine learning blocksgradient boosting classifiers and regression‐type artificial neural networks—are trained, using Python libraries, to identify the best ADC architecture as well as to infer a set of design parameters which yields ADC specifications. The result from the ML blocks can be cross‐checked in behavioural simulations in MATLAB and also optimized with respect to signal‐to‐noise ratio (SNR), power consumption, or figure‐of‐merit (FoM) using an embedded simulated annealing (SA) process. The toolbox is controlled through a graphical user interface (GUI) for MATLAB which guides the designer through the whole process, from specifications to obtaining an implementation that meets the required specifications.