AI-Powered Enhancement of Sigma Delta ADC: The Role of Artificial Neural Network-Based Optimization Systems

Abstract

This paper presents the application of Artificial Intelligence (AI) to automate the intricate process of designing Sigma-Delta Analog-to-Digital Converters (SD-ADC). It explores the integration of Artificial Neural Networks (ANN) to augment the design, optimization, and operation of Sigma Delta Modulators (SDM). The core concept focuses on training ANN models to achieve the optimal design parameters for SD-ADCs. Traditional SD-ADC design approaches typically involve an iterative simulation process, wherein circuit design parameters are adjusted until the desired performance is achieved. However, this approach can be time-consuming and computationally demanding, especially when dealing with the complex SD-ADCs architectures. In response, this article proposes an alternative by replacing this manual process with the assistance of ANN models. The Proposed methodology comprises two phases. In Phase 1, the trained ANN model predicts the SD-ADC output parameters, while Phase 2 focuses on estimating the optimal input design parameters of the SD-ADC. Remarkably, the circuit’s desired parameters are derived from ANN model predictions rather than the traditional method at the architectural level. The comparative analysis between ANN, Machine Learning (ML), and traditional optimization models is performed to validate the effectiveness of the proposed model.

Keywords

• Sigma-delta modulators
• analog-to-digital converters
• artificial intelligence
• artificial neural networks
• machine learning
• circuit architectures