A Reconfigurable CMOS Mixer with Adaptive Gain and Linearity for Native-AI RF Systems
Conference
Uddin, A, Gadea, JL, Madanayake, A et al. (2026). A Reconfigurable CMOS Mixer with Adaptive Gain and Linearity for Native-AI RF Systems
. Midwest Symposium on Circuits and Systems, 900-904. 10.1109/MWSCAS67364.2026.11680813
Uddin, A, Gadea, JL, Madanayake, A et al. (2026). A Reconfigurable CMOS Mixer with Adaptive Gain and Linearity for Native-AI RF Systems
. Midwest Symposium on Circuits and Systems, 900-904. 10.1109/MWSCAS67364.2026.11680813
Emerging AI/ML-driven wireless systems demand RF front-ends with dynamically reconfigurable performance rather than fixed operating points. To address this need, we present a reconfigurable double-balanced folded cascode CMOS active mixer designed in a 65 nm CMOS process. Dual operating modes allow flexible trade-offs among conversion gain (CG), noise figure (NF), and linearity, while a digitally controlled current switching circuit (CSC) enables real-time parameter tuning via a 6-bit control word. Pre-layout simulations over 1-7 GHz show a peak CG of 18.1 dB, an input-referred 1 dB compression point of 0 dBm, and a minimum NF of 9.5 dB. A Dueling Double Deep Q-Network (DQN) agent, trained on a differentiable digital twin of the mixer, validates AI-readiness by autonomously selecting optimal mode and switching current configurations across varying signal power and channel conditions in real time.
