MRAM research or technical information

Samsung researchers are first to demonstrate MRAM-based in-memory computing

Researchers from Samsung's Advanced Institute of Technology (SAIT), have demonstrated what they say is the world’s first in-memory computing based on MRAM, targeting next-generation AI chips.

The researchers explain that In-Memory computing is a new paradigm that seeks to perform both data storage and data computing in a memory network. In such a computing system, a large amount of data, stored in the memory network, can be executed in a highly parallel manner. Power consumption in such systems is substantially reduced.

Researchers use MIFL to increase the magnetoresistance of pMTJs

Researchers from the University of Arizona developed a new pMTJ structure that exhibits high magnetoresistance, strong retention and is likely to achieve fast switching times as well.

pMTJ with multi-interface free layer (Arizona University)

The new structure uses a multi-interface free layer (MIFL) which incorporate multiple materials with different properties. The researchers used a ferromagnetic CoFeB layer with nonmagnetic Mo or MgO layers. The magnetoresistance can be controlled by changing the thickness of the CoFeB layer. The researchers managed to demonstrate a magnetoresistance of over 200%.

Researchers from Tohoku University developed the world's smallest STT-MRAM MTJ

Researchers from Tohoku University managed to fabricate the world's smallest STT-MRAM MTJ, at 2 nm. In addition, the researchers demonstrated fast switching (3.5 ns) in sub-five-nm STT-MRAM MTJs.

10-nm MTJ TEM photo, Tohoku University

The new MTJ were developed using a new multilayered ferromagnetic structure that can engineer characteristic relaxation time, which governs the magnetization dynamics in the ns regime.

New SOT-MRAM device structure can be scaled up and is highly efficient

Researchers from Northwestern University, in collaboration with researchers from China, Italy and France, developed a new SOT-MRAM device structure that enables deterministic switching without any need for bias magnetic fields.

The new approach, unlike most earlier methods, can be scaled to large wafers with good uniformity, since it doesn't rely on having a structural asymmetry in the device. SOT-MRAM devices based on this structure could be faster and more energy-efficient than current designs.

Researchers suggest using stochastic MRAM elements to create highly efficient AI neural network devices

Researchers from Northwestern University developed a new method of building artificial neural networks using MRAM-based stochastic computing units. The researchers say that this design could enable AI devices that are highly energy efficient.

MTJ-based stochastic computing unit structure (Northwestern University)

Embedded MRAM technologies are being adopted at major foundries, which enable the use of these technologies for unconventional computing architectures that use the stochasticity of MRAM cells (rather than their nonvolatility), to perform energy-efficient computing operations. MRAM cells exhibit stochastic switching characteristics, which is a challenge for reliable memory devices. But for neural networks, this can be taken advantage of if the MTJs are appropriately designed.

Researchers developed a promising antiferromagnetic MRAM device structure

Researchers from Northwestern University and the University of Messina in Italy developed a new MRAM memory device composed of antiferromagnetic materials, which could be beneficial for use in AI systems and cryptocurrency mining.

Magnetic switching with antiferromagnet IrMn3 - device design

Antiferromagnetic materials (AFM), offer inherently faster dynamics than ferromagnetic materials (FM), have no macroscopic magnetic poles and can be scaled much better. AFM-based memory cannot be erased with external magnetic fields which could prove to be a major security advantage.

IBM to reveal the world's first 14nm STT-MRAM node

IBM announced that during the 2020 IEEE International Electron Devices Meeting (IEDM 2020), that is now being held virtually, its researchers will reveal the first 14 nm node STT-MRAM. IBM says that efficient and high-performance STT-MRAM systems will help to address memory-compute bottlenecks in hybrid cloud systems.

IBM says that the 14 nm node embedded MRAM which will be revealed is the most advanced MRAM demonstrated to date. It features circuit design and process technology that could soon enable system designers to replace SRAM with twice the amount of MRAM in last-level CPU cache.

Researchers find that FGT is an excellent material for SOT-MRAM devices

Researchers from Seoul's National University and Pohang's University of Science and Technology (POSTECH) report that a 2D iron germanium telluride (Fe3GeTe2, or FGT) layer is an excellent candidate to be used as the basis SOT-MRAM material.

Fe3GeTe2-based SOT-MRAM device structure (POSTECH / SNU)

An SOT-MRAM based on FGT is highly energy-efficient, in fact the researchers say that the measured magnitude of SOT per applied current density is two orders of magnitude larger than the values reported previously for other candidate materials.

Researchers develop the world's smallest high-performance MTJ

Researchers from Tohoku University say they have developed the world's smallest (2.3 nm) high-performance magnetic tunnel junctions (MTJs).

Shape anisotropy MTJ scheme (Tohoku University)

The design is based on the Shape-anisotropy MTJ (developed by the same researchers in 2018) in which thermal stability is enhanced by making the ferromagnetic layer thick. In this new research, the scientists used a new structure that uses magnetostatically coupled multilayered ferromagnets - which enabled the scaling down to 2.3 nm diameters.