Single Germanium MOSFET-Based Low Energy and Controllable Leaky Integrate-and-Fire Neuron for Spiking Neural Networks

In this work, a single transistor based on germanium (Ge) is used to construct a leaky integrate-and-fire (LIF) neuron with significant improvement in energy efficiency, area efficiency, and reduction in cost. Using 2-D calibrated simulation, we validated that Ge-mosfet LIF neuron is able to imitate the neuron behavior accurately. The Ge-mosfet shows low breakdown voltage, high impact ionization coefficient, and sharp breakdown. All these factors are responsible for achieving low energy per spike and higher spiking current. The proposed Ge-mosfet-based spiking LIF neuron needs only 8 pJ/spike of energy as compared to recently reported silicon-based silicon-on-insulator (SOI) mosfet, which needs 45 pJ/spike of energy. The use of gate voltage makes Ge-mosfet LIF neuron firing controllable, which can improve the energy efficiency of the spiking neural network (SNN) by inducing sparse action.

Automated summary

In this work, a single transistor based on germanium (Ge) is used to construct a leaky integrate-and-fire (LIF) neuron, providing significant improvements in energy efficiency, area efficiency, and reduction in cost. Through 2-D calibrated simulation, it is validated that the Ge-mosfet LIF neuron accurately imitates the behavior of a neuron. The Ge-mosfet exhibits low breakdown voltage, high impact ionization coefficient, and sharp breakdown, contributing to low energy per spike and higher spiking current. Compared to a recently reported silicon-based silicon-on-insulator (SOI) mosfet, the proposed Ge-mosfet LIF neuron requires only 8 pJ/spike of energy. The use of gate voltage allows for controllable firing of the Ge-mosfet LIF neuron, improving the energy efficiency of the spiking neural network (SNN) by inducing sparse action.