Machine Learning based Adaptation for CTLE of Serial Links

High-speed communication systems face challenges due to channel impairments, necessitating advanced equalization techniques for signal recovery. This work presents a novel approach to address these challenges through the implementation of a machine learning (ML) based adaptation of the parameters of a continuous-time linear equalizer (CTLE) receiver. The proposed receiver leverages machine learning models to estimate channel loss based on pulse response, facilitating the adaptation of CTLE parameters for optimal signal recovery. A genetic algorithm (GA) is employed to optimize CTLE parameters, enhancing the receiver’s performance under varying channel conditions. The implemented receiver architecture incorporates feedforward neural network (FNN) machine learning technique with adaptive CTLE. The proposed approach is evaluated using simulation experiments with demonstration of its effectiveness in improving the signal quality. This work contributes to the analog design domain by bridging ML techniques with traditional equalization methods.