Overview
The Magnetic Random Access Memories (MRAM) group develops advanced concepts in this emerging technology. The goal is to realize cells with improved thermal stability, lower power consumption and/or faster switching. Our research covers material stack deposition, nano-fabrication and electrical test evaluation, for applications as standalone memory and non-volatile logic and more recently in neuromorphic computing architectures.
Research directions
Perpendicular Anisotropy Materials
High energy barriers for spin transfer torque (STT) MRAM cells can be achieved with perpendicular anisotropy magnetic tunnel junctions. Solutions for high density MRAM cells to diameters below 20nm require continuous improvements in perpendicular surface anisotropy, while maintaining high TMR properties.
Perpendicular STT MRAM
Evaluation of MRAM concepts requires simulation of expected reversal mechanisms and electrical characterization of individual cells. We aim at understanding dynamics of magnetization reversal and the expected impact of stack modifications to explore application specific optimizations.
Nanofabrication Challenges
Innovation on dense MRAM using pre-patterned substrates, CMOS integration of multifunctional cells and sub-10nm lateral sizes. Tunnel junction nanofabrication in our platform is essential to evaluate MRAM concepts and performance.
Perpendicular Shape Anisotropy
A solution for sub-10nm cell sizes uses high aspect ratios to generate perpendicular shape anisotropy providing scalable retention at the smallest cell sizes. Spin transfer torque switching is possible in these cells, where the reversal dynamics is now under study.
The team
Former members
Post-docs
- Andrey TIMOPHEEV (2014-2017)
- Van Dai NGUYEN (2016-2018)
- J. Ranier Roiz (2015-2016)
- Nikita Strelkov (2016-2019)
PhD
- Luc TILLIE (2015-2018)
- Nicolas PERRISSIN (2015-2018)
- Jyotirmoy CHATTERGEE (2014-2017)
- Hieu Tan NGUYEN (2013-2016)
- Antoine Chavent (2013-2015)
Process Engineers
- Jude GUELFFUCCI (2015-2017)
- Nathalie LAMARD (2016-2017)
- Guillaume LAVAITTE (2015-2016)
Projects
- Samsung SGMI (2014-2017)
- ANR Excalyb (2014-2017)
- Heumem (2015-2018)
- EU-FET Spice (2016-2019)
- EU Great (2016-2019)
- ERC Magical (2015-2020)
Partners
- CEA LETI, Grenoble, France
- Institut NEEL, Grenoble, France
- Crocus Technology, Grenoble, France
- Samsung, San Jose, USA
- Singulus AG, Kahl am Main, Germany
- Aarhus University, Aarhus, Denmark
- Radboud Universiteit, Neijmegen, Netherlands
Recent news
- Two years post-doctoral position on modelling of all optical switching using light carrying orbital momentum (June 23rd, 2022)
SPINTEC is opening a 2-year post-doctoral position on modelling of all optical switching using light carrying orbital momentum Context: The manipulation of magnetization by ps or fs light pulses has become a very exciting topic nowadays ... - Seminar – Spins, Bits, and Flips: Essentials for High-Density Magnetic Random-Access Memory (June 20th, 2022)
On Thursday, July 7 at 14:00 we have the pleasure to welcome Dr. Tiffany SANTOS from Western Digital Corporation, CA, USA. She is 2022 IEEE Magnetics Society Distinguished Lecturer and will give us a seminar ... - PhD defense – Spintronic devices with reduced content of Platinum and Ruthenium (May 29th, 2022)
On Tuesday, June 7th at 14:00, M. Alvaro PALOMINO will defend his PhD thesis entitled: Spintronic devices with reduced content of Platinum and Ruthenium Place : CEA Bat. 10.05 Room 445 Zoom link https://univ-grenoble-alpes-fr.zoom.us/j/9618654716?pwd=MFNPYXRyU1N0R282d3lOYUovWm1HQT09 See access conditions at the end Abstract ... - Designing magnetic memory with improved retention and writability (April 08th, 2022)
Magnetic Random Access Memory recently started to be commercialized by all main microelectronics factories. In MRAM, the information is coded via parallel or antiparallel magnetic configurations to represent ones and zeroes. The technology is intrinsically ... - STOCHNET – An ANR project (December 09th, 2021)
STOCHNET stands for Hybrid Stochastic Tunnel Junction Circuits for Optimization and Inference. The motivation behind StochNet is to explore — through experimental demonstrations with hybrid CMOS stochastic tunnel junction circuits and simulations of theoretical ...