Evolution Strategy and Controlled Residual Convolutional Neural Networks for ADC Calibration in the Absence of Ground Truth

Calibrating ADCs in the absence of ground truth presents a significant challenge for high-precision applications. This paper addresses this issue by introducing a novel two-step approach that combines evolutionary strategy and deep learning techniques. First, we employ covariance matrix adaptation evolution strategy to obtain ground truth signal samples with optimal SFDR values. This serves as a robust foundation for the subsequent calibration process. Second, we propose a new calibration neural network architecture called controlled residual convolutional neural networks. This architecture introduces a controlled residual branch within the network, allowing for more effective learning and calibration. The controlled residual branch is designed to adaptively adjust the network’s focus between the main and residual paths, thereby enhancing its calibration capabilities. Experimental results underscore the efficacy of our proposed method. Specifically, we observed a 29.01dB improvement in SFDR, representing the maximum enhancement relative to previous methods. These results validate the effectiveness of our approach in achieving high-precision ADC calibration without the need for the information of ground truth signals, thereby making it feasible for background calibration.