FERROELASIC THIN FILMS WITH DOMAIN WALL NETWORKS AS SELF-LEARNING MATERIALS?

SALVERDA, MART; D RUBI; HASRAT, S; ACEVEDO, W ROMÁN; M. RENGIFO; S. FAROKHIPOOR; B NOHEDA

Congreso; Nature Conference on Ferroic Materials: Challenges and Oportunities; 2017

Modern technology generates huge amounts of data that can be tuned into valuable information capable of transforming our society: from diagnostic and prevention of health issues to forecasting of financial markets or nature disasters. At the moment, however, processing of large amounts of unstructured data can only be done using supercomputers with high power consumption and thus, much of the available data remains underused. That is why now more than ever, non-von-Neumann computer architectures are being considered. Particularly attractive is the concept of neuromorphic computing (NC) that aims to emulate the human brain and its unique capability to process large amounts of complex sensory data using orders of magnitude less power than our computers. Even though NC used to involve mainly software development and expertise in neural networks and machine learning, recently, materials scientists, physicist and chemists have become part of these efforts by developing memristive materials that are able to emulate the adaptability of the brain[1-3]. It is becoming clear that a key breakthrough in NC needs to come from the synthesis of a material that can learn by itself. Ferroic oxides hold unique possibilities in this respect[4]. Here we will discuss domain wall networks in ferroelastic oxides and their promise as self-learning materials.[1] D.B. Strukov, G. S. Snider, D. R. Stewart & R. S. Williams, Nature 453, 80 (2008)[2] I. Valov, E. Linn, S. Tappertzhofen, S. Schmelzer, J. van den Hurk, F. Lentz & R. Waser, Nature Comm. 4, 1771 (2013)[3] A. Wedig, M. Luebben, D-Y. Cho, M. Moors, K. Skaja, V. Rana, T. Hasegawa, K. K. Adepalli, B. Yildiz, R. Waser & Ilia Valov, Nature Nanotechnoloty 11, 67 (2016) [4] A. Chanthbouala, V. Garcia, R.O. Cherifi, K. Bouzehouane, S. Fusil, X. Moya, S. Xavier, H. Yamada, C. Deranlot, N. D. Mathur, M. Bibes, A. Barthelemy, J. Grollier, Nature Materials 11, 860 (2012).