Realization of Nonlinear Logic Functions via Ge-MOSFET-Based Leaky Integrate-and-Fire Neurons

Spiking neural networks (SNNs) are promising for artificial intelligence and edge computing due to their biological plausibility, efficiency, and low power consumption. However, implementing nonlinear logic functions like XOR and XNOR typically requires multilayer networks or complex circuits, limiting energy efficiency and integration density. This paper presents a leaky integrate-and-fire (LIF) neuron based on germanium-channel MOSFETs (Ge-MOSFETs). By optimizing synaptic weight configuration and leveraging germanium's low bandgap and high carrier mobility, nonlinear logic is achieved through a parallel dual-neuron structure within a single-layer network. Experimental results show ultra-low energy consumption of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.47 \times 10^{-19} \mathrm{J}$</tex> per pulse. Additionally, integration time constants can be flexibly controlled via input current modulation. These features support the development of compact, energy-efficient neuromorphic hardware and advance the integration of low-power SNN systems.