Frequency Behaviour of FeFET-Based Ultra-Low-Power Coupled Oscillator Neurons

The inefficiencies of von-Neumann computing architectures in performing high-level machine learning and neuromorphic operations have led to the need for new computational systems such as oscillatory neural networks, that are designed using emerging technologies. The frequency dynamics of Ferroelectric field-effect transistor (FeFET)-based coupled oscillator neurons, as integral parts of these novel computational networks are investigated in this paper. The concept of time to frequency modulation is also introduced which can be used to distinguish dynamical changes in the incoming signals. Simulation results show a significant reduction in power consumption with approximately 9 μW for each oscillator neuron. This is significant improvement over results reported for spin-torque and metal-insulator-migration oxide-based coupled-oscillators. Integrability with CMOS technology in conjunction with lower power consumption and simpler topologies make FeFET devices promising candidates for the next generation of neuromorphic computing platforms, and particularly for image, video, and audio processing tasks.