Hybrid Stochastic Divider-Based Delta-Sigma Data Convertor Using Linearity Enhancement of Multi-channel ADC with Robust Control Mechanism

The article delves into the critical role of analog-to-digital converters (ADCs) in modern digital systems, emphasizing the advantages and limitations of Delta-Sigma Modulators (DSMs). It addresses the challenges faced by traditional DSM architectures, such as stability at high oversampling ratios, power consumption, and the trade-off between resolution and conversion efficiency. The paper introduces a Hybrid Stochastic Divider Delta-Sigma Modulator, which leverages stochastic quantization and robust control mechanisms to enhance the performance of multi-channel ADCs. Key innovations include the Hysteresis Split Source Comparison-based Quantizer, which improves signal fidelity and reduces quantization errors, and the Robust Feedback Compression Controller, which addresses quantization errors, slope overload distortion, and high latency issues. The article also discusses the integration of machine learning techniques, such as Generative Adversarial Networks (GANs), to further optimize the ADC performance. Through extensive experimental results and comparisons with existing methods, the paper demonstrates the superior efficiency, accuracy, and signal integrity achieved by the proposed model. The findings highlight the potential of the hybrid stochastic approach in revolutionizing data conversion technologies for high-performance digital applications.