Coincidence Detection with an Analog Spiking Neuron Exploiting Ferroelectric Polarization

The ability to detect correlated events in the environment is an important feat of biological neural networks. Neuromorphic computing strives to mimic this ability for efficient sensory processing. For this purpose, we propose a HfO
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based ferroelectric capacitor (FeCap)-complementary metal oxide semiconductor (CMOS) leaky integrate-and-fire (LIF) neuron able to detect highly correlated events exploiting two different temporal dynamics. The possibility to exploit two time constants increases the versatility of the neuron and its dynamic adaptation while offering a compact and elegant solution for detection of both transient and sustained coincidences. Moreover, the time constants are in biologically relevant time scales, which makes the neuron suitable to solve real-time tasks such as keyword spotting or sensory processing. The proposed FeCap-based LIF (FeLIF) neuron enriches the dynamic of a standard LIF neuron fostering the development of advanced event-based analog neuromorphic hardware.