A Machine Learning Driven PVT-Robust VCO with Enhanced Linearity Range

This work presents a PVT robust machine learning-based Voltage-controlled Oscillator (VCO) with an enhanced linearity range. The machine learning algorithm with PVT robustness is implemented digitally. Different from conventional methods, the proposed scheme does not require the VCO to be in working mode every time one needs the prediction of frequency. The proposed scheme uses the frequency to digital converter (FDC) output data as an input learning vector and uses a prediction block to predict the future frequencies. An 11-stage voltage-controlled oscillator with a machine learning algorithm is implemented in SCL 180 nm CMOS technology. The measurement results show that the proposed architecture is robust against PVT variations with an enhanced linearity range. Without a machine learning algorithm, the VCO's control voltage linearity range is 0.28 V to 0.40 V that increases to the range from 0.1 to 1.8 V after applying the proposed machine learning algorithm. The maximum gain variation of 3.71% is observed at FF with respect to the TT corner after applying the proposed machine learning algorithm.