Enhancing Performance of SAR ADC through Supervised Machine Learning

Over the years, successive approximation register (SAR) analog-to-digital converter (ADC) designs have adopted different techniques to correct static and dynamic errors. This work proposes a machine-learning (ML) approach that uses a single model to correct both static and dynamic errors without requiring prior knowledge of these errors or complex design efforts. The proposed technique derives a custom feature set from the ADC output and uses supervised learning technique with a low-speed reference ADC to learn a representation of the ADC errors and correct them continuously in the back-end. The proposed ML correction is demonstrated on a 10-bit SAR ADC fabricated in 65nm CMOS and improves SNDR and SFDR by more than 8dB and 25dB respectively. The proposed ML technique adopts online learning and adaptively updates all the model weights individually to achieve fast convergence.