Stability Prediction of ΔΣ Modulators using Artificial Neural Networks

This paper introduces an Artificial Neural Network (ANN) to predict the stability of Delta-Sigma modulators (DSMs) and, furthermore, shows its beneficial employment in a genetic optimization algorithm. Since a DSM is a non-linear system, its stability often can't be predicted by simple algebra. Therefore, a new approach predicting the stability of DSMs using an ANN is presented in this work. It is shown how the data generation and training of such a network can be done. Furthermore, the derivation of high-level coefficients for DSMs is a tedious task, which is often solved by time consuming simulations. The application of the derived ANN in a genetic algorithm to find these high-level coefficients leads to the significant time savings of close to 50%.