Temporally Correlated Stochastic Spiking Neural Networks Enabled by 2D Ferroelectric Synapse-Neuron Integration

Stochastic neural computation is essential for probabilistic inference and brain-inspired information processing. However, most hardware stochastic neurons generate statistically independent spikes and fail to reproduce the temporal correlations observed in biological neurons. We demonstrate an integrated hardware synapse-neuron consisting of a FeFET synapse and a CulnP₂S₆ neuron, where the neuron exhibits temporally correlated stochastic spiking. The spike probability is continuously tuned by synaptic weight through voltage modulation, enabling hardware stochastic neural computation. Electrical measurements reveal voltage-dependent stochastic firing accompanied by leaky-integration-firing dynamics. Structural and polarization analyses reveal differences in Cu-related atomic contrast and partially retained polarization states, suggesting a history-dependent internal state. Autocorrelation and inter-spike interval analyses reveal temporal dependencies between successive stochastic spikes, which are consistent with the evolution of this retained state, with the strongest correlation emerging in the intermediate stochastic regime. The proposed stochastic spiking neural network achieves an inference accuracy of approximately 90% and maintains over 87% accuracy under severe noise conditions, demonstrating the computational advantages of temporally correlated stochastic spiking for robust probabilistic neural computation. This work demonstrates a class of 2D ferroelectric neuromorphic hardware that inherently incorporates intrinsic temporal dynamics, providing a pathway toward next-generation neuromorphic and probabilistic computing.