Learning through ferroelectric domain dynamics in solid-state synapses.

In the brain, learning is achieved through the ability of synapses to reconfigure the strength by which they connect neurons (synaptic plasticity). In promising solid-state synapses called memristors, conductance can be finely tuned by voltage pulses and set to evolve according to a biological learning rule called spike-timing-dependent plasticity (STDP). Future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Here we report on synapses based on ferroelectric tunnel junctions and show that STDP can be harnessed from inhomogeneous polarization switching. Through combined scanning probe imaging, electrical transport and atomic-scale molecular dynamics, we demonstrate that conductance variations can be modelled by the nucleation-dominated reversal of domains. Based on this physical model, our simulations show that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way, opening the path towards unsupervised learning in spiking neural networks.

Plasticity in memristive devices for spiking neural networks.

Memristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use.

Giant electroresistance of super-tetragonal BiFeO3-based ferroelectric tunnel junctions.

Ferroelectric tunnel junctions enable a nondestructive readout of the ferroelectric state via a change of resistance induced by switching the ferroelectric polarization. We fabricated submicrometer solid-state ferroelectric tunnel junctions based on a recently discovered polymorph of BiFeO3 with giant axial ratio ("T-phase"). Applying voltage pulses to the junctions leads to the highest resistance changes (OFF/ON ratio >10,000) ever reported with ferroelectric tunnel junctions. Along with the good retention properties, this giant effect reinforces the interest in nonvolatile memories based on ferroelectric tunnel junctions. We also show that the changes in resistance scale with the nucleation and growth of ferroelectric domains in the ultrathin BiFeO3 (imaged by piezoresponse force microscopy), thereby suggesting potential as multilevel memory cells and memristors.