Leaky integrate-and-fire neurons based on perovskite memristor for spiking neural networks

Abstract

Artificial neuron is an important part of constructing neuromorphic network in which information can be computed with high parallelism and efficiency like in the human brain. However, owing to the poor biological plausibility, artificial neurons based on traditional complementary metal-oxide-semiconductor (CMOS) platform fail to reveal the rich ion dynamics of the biological counterparts. Organic–inorganic halide perovskites (OHPs) are prospective for imitating the ion dynamics on the membranes of biological neurons because of its intrinsic ion migration. Herein, a diffusive CH 3 NH 3 PbI 3 (MAPbI 3 )-based memristor with superior amplitude-frequency characteristics and highly linear conductivity modulation for more than 1000 states have been fabricated for the construction of a leaky integrate-and-fire (LIF) bio-inspired neuron. The as-designed LIF model can successfully emulate the leakage, spatiotemporal integration and firing functions in a biological neuron. Moreover, by connecting LIF neurons with a 2*2 non-volatile Al 2 O 3 -based synaptic array, a simple spiking neural network (SNN) which is called the 3rd generation of neural network has been implemented at the hardware level to study the cognitive performance of the network. The SNN exhibits outstanding selective sensitivity to particular input sequence, indicating the excellent adaptability and versatility of the network for future applications of neuromorphic computing by utilizing novel ionotropic device. Herein, for the first time, a diffusive CH 3 NH 3 PbI 3 (MAPbI 3 )-based memristor with superior amplitude-frequency characteristics and highly linear conductivity modulation for more than 1000 states have been fabricated for the construction of a leaky integrate-and-fire (LIF) bio-inspired neuron. The as-designed LIF model can successfully emulate the leakage, spatiotemporal integration and firing functions in a biological neuron, indicating the great potential for neuromorphic computing. Furthermore, by connecting LIF neurons with a 2*2 non-volatile Al 2 O 3 -based synaptic array, a simple spiking neural network has been implemented at the hardware level to study the cognitive performance of the network. • The conductance states of MAPbI 3 -based memristor can be modulated linearly and consecutively for more than 1000 states. • The memristor exhibits superior amplitude-frequency response characteristics which may be also suitable for filter. • A leaky integrate-and-fire artificial neuron is implemented, and the leakage, spatiotemporal integration and firing functions are emulated successfully. • By combining the artificial neuron with an synaptic array, a simple spiking neural network has been implemented.