Arxiv (24 pages, researchers from France, Japan and the UK)- Memristors are continuously tunable resistors that emulate synapses. Conceptualized in the 1970s, they traditionally operate by voltage-induced displacements of matter, but the mechanism remains controversial. Purely electronic memristors have recently emerged based on well-established physical phenomena with albeit modest resistance changes. Here we demonstrate that voltage-controlled domain configurations in ferroelectric tunnel barriers yield memristive behaviour with resistance variations exceeding two orders of magnitude and a 10 ns operation speed. Using models of ferroelectric-domain nucleation and growth we explain the quasi-continuous resistance variations and derive a simple analytical expression for the memristive effect. Our results suggest new opportunities for ferroelectrics as the hardware basis of future neuromorphic computational architectures.
In summary we have reported transport measurements in ferroelectric tunnel junctions (FTJ) as a function of the amplitude, duration and number of voltage pulses. The resistance can be continuously and reversibly tuned over more than two orders of magnitude by varying the pulse amplitude and/or the pulse number (and thus the total integrated excitation time). These features qualify FTJs as memristive devices. This improves upon previous memristors with a purely electronic mechanism where the resistance contrast is no better than a factor of two. Relying on the correlation between junction resistance and ferroelectric domain structure (as imaged by PFM), we model the resistive switching behaviour using a simple model of domain nucleation and growth in a heterogeneous medium. We derive an analytical expression ruling the memristive response, which exemplifies the advantage of resorting to well-established physical phenomena like ferroelectricity in the design of novel memristive systems. Our results invite additional investigations of switching dynamics in nanoscale ferroelectrics and open unforeseen perspectives for ferroelectrics in next-generation neuromorphic computational architectures.