High-Level Design of Sigma-Delta Modulators Using Artificial Neural Networks

This paper presents a high-level synthesis methodology for Sigma-Delta Modulators ( $\SD$ Ms) that combines behavioral modeling and simulation for performance evaluation, and Artificial Neural Networks (ANNs) to generate high-level designs variables for the required specifications. To this end, comprehensive datasets made up of design variables and performance metrics, generated from accurate behavioral simulations of different kinds of $\SD$ Ms, are used to allow the ANN to learn the complex relationships between design-variables and specifications. Several representative case studies are considered, including single-loop and cascade architectures with single-bit and multi-bit quantization, as well as both Switched-Capacitor (SC) and Continuous-Time (CT) circuit techniques. The proposed solution works in two steps. First, for a given set of specifications, a trained classifier proposes one of the available $\SD$ M architectures in the dataset. Second, for the proposed architecture, a Regression-type Neural Network (RNN) infers the design variables required to produce the requested specifications. A comparison with other optimization methods – such as genetic algorithms and gradient descent – is discussed, demonstrating that the presented approach yields to more efficient design solutions in terms of performance metrics and CPU time.