2024-08-18 06:00:12
Shrinking the size of computer components to a near-atomic level is no longer a dream, but an approaching reality thanks to a new technique.
Researchers have just demonstrated that it is possible to significantly miniaturize processors by exploiting magnetic states in 2D materials, an advance that could revolutionize the energy efficiency of future computing systems.
The semiconductor industry’s major challenge is to continually reduce the size of transistors while increasing computing power. However, the physical limitations of silicon pose a difficult obstacle to overcome. This is where spintronics, a technology using the spin states of electrons to represent bits of data binaries, paving the way for much denser and more energy-efficient components energy. This innovation is based on magnetic tunnel junctions (MTJ), where the material used, chromium triiodide, acts as a magnet insulating 2D. By precisely controlling the electric currentthe researchers were able to manipulate theorientation magnetic properties of this material, thus making it possible to represent the binary states essential to any system computer science. This could potentially increase tenfold the density computing chips, while drastically reducing energy consumption during the switching process.
The concept of spintronics is not new, but precise control of the thickness of material layers and the quality of their interfaces remains a challenge. The innovation lies in the ability to pass extremely dense currents through these junctions, while meeting the needs of miniaturization and energy efficiency, imperatives for future computer systems.
Despite these promising advances, challenges remain, including the need to maintain temperatures close to absolute zero for these devices to function properly. This factor currently limits large-scale practical applications, but the potential energy gains justify continued research in this direction.
This discovery opens up new perspectives for future technologies, particularly for energy-hungry artificial intelligence systems, where each gain in efficiency could represent a major advance.
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