Magnetic Tunnel Junction

Magnetic tunnel junctions (MTJs) are nanoscale devices exhibiting a resistance change dependent on the relative magnetization of their layers, making them attractive for various applications. Current research focuses on leveraging MTJs' properties in neuromorphic computing, particularly for building energy-efficient artificial neurons and synapses within spiking neural networks and computational random-access memory (CRAM) architectures. These efforts involve developing hardware-aware training algorithms to mitigate device imperfections and exploring different model architectures, such as integrate-and-fire neurons and reservoir computing, to optimize performance. The potential impact lies in creating significantly more energy-efficient and powerful computing systems for machine learning and other demanding applications.