An integrated microwave neural network for broadband computation and communication

Abstract

The development of high-bandwidth applications, including multi-gigabit communication and radar imaging, demands faster processing. However, in the microwave regime, where frequencies exceed clock rates, sampling and computation become challenging. Here we report an integrated microwave neural network for broadband computation and communication. Our microwave neural network operates across tens of gigahertz but is reprogrammed with slow megabits per second control bitstreams. By exploiting strong nonlinearity in coupled microwave oscillations, it expresses its computation in a narrower spectrum, enabling easy read-out. The system searches bit sequences in multi-gigabits per second data and emulates digital functions without custom circuits. It accelerates radio-frequency machine learning by classifying encoding schemes and detecting frequency shifts to track flight trajectories from radar. The microwave neural network is fabricated with standard complementary metal–oxide–semiconductor technology. It occupies a sub-wavelength footprint of 0.088 mm2 on chip and has a sub-200-mW power consumption, supporting integration in a general-purpose analogue processor. A low-power microwave neural network fabricated using complementary metal–oxide–semiconductor technology can perform broadband computations using a slow control mechanism.