A ferroelectric memristor

• Published in: Nature materials Vol. 11; no. 10; pp. 860 - 864
• Main Authors: Chanthbouala, André, Garcia, Vincent, Cherifi, Ryan O., Bouzehouane, Karim, Fusil, Stéphane, Moya, Xavier, Xavier, Stéphane, Yamada, Hiroyuki, Deranlot, Cyrile, Mathur, Neil D., Bibes, Manuel, Barthélémy, Agnès, Grollier, Julie
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.10.2012; Nature Publishing Group
• Subjects: 639/301/119/996; Biomaterials; Chemistry and Materials Science; Condensed Matter; Condensed Matter Physics; Electronics; Exact solutions; Ferroelectric materials; Ferroelectricity; Ferroelectrics; Hardware; letter; Materials Science; Mathematical analysis; Mathematical models; Nanotechnology; Nucleation; Optical and Electronic Materials; Physics; Resistors
• ISSN: 1476-1122; 1476-4660; 1476-4660
• DOI: 10.1038/nmat3415

Memristors are devices whose dynamic properties are of interest because they can mimic the operation of biological synapses. The demonstration that ferroelectric domains in tunnel junctions behave like memristors suggests new approaches for designing neuromorphic circuits. Memristors are continuously tunable resistors that emulate biological synapses 1 , 2 . Conceptualized in the 1970s, they traditionally operate by voltage-induced displacements of matter, although the details of the mechanism remain under debate 3 , 4 , 5 . Purely electronic memristors based on well-established physical phenomena with albeit modest resistance changes have also emerged 6 , 7 . Here we demonstrate that voltage-controlled domain configurations in ferroelectric tunnel barriers 8 , 9 , 10 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 11 , 12 , 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.