09/07/2019
10h00 – IEMN Laboratoire Central – Salle du conseil
Campus Cité Scientifique – Villeneuve d’Ascq

Rachid SBIAA
Professeur à l’Université Sultan Qaboos d’Oman

Abstract :

For more than a half-century, the electronics industry was driven by Moore’s law which may be facing its end in few years time. Therefore, researchers are looking at new materials such as spin-based materials and architectures that are different from von Neumann architecture for the growth of the semiconductor industry. An emerging technology called magnetic domain wall memory or racetrack memory is widely considered to be a potential successor for conventional hard disk drives or flash memories. Instead of storing information in the orientation of magnetic spins, a magnetic domain wall memory device stores bits in a three-dimensional chain of magnetic domains lying along nanometer-scale wires, or « racetracks ». In this presentation, I will give an introduction to magnetic memory MRAM, its advantages and limitations. In the second part of the talk, I will present our results on a new type of domain wall-memory devices where the magnetic state could be controlled and predicted accurately.

Biography:

Dr. Rachid SBIAA is an Associate Professor at Sultan Qaboos University since 2012. He obtained his Ph.D. in 1996 from Paris University in the field of Materials Science. After his graduation, he worked as a research fellow in the laboratory of magnetism and thin films, CNRS (France). In 2002, he joined TDK-Japan to work on spintronics and magnetoresistive sensors. He designed magneto-resistive sensors for hard disk drives and devices for magnetic memory. In June 2006, he joined the Data Storage Institute in Singapore where he was acting as Program leader for magnetic memory devices. His expertize is in nanoelectronic devices and magnetic materials. He has about 50 USA and Japanese patents and 120 research papers in the field of magnetism and Spintronics. His current research work is focused on magnetic domain wall devices for memory and neuromorphic computing.