{
  "id": 7420202,
  "title": "Room-temperature skyrmion-based synapses could pave the way for energy-efficient AI",
  "url": "https://urgent.news/2026/09/14/room-temperature-skyrmion-based-synapses-could-pave-the-way-for",
  "topic": "science",
  "section": "Science",
  "published": "2026-09-14T23:20:01.000Z",
  "source": {
    "name": "Phys.org",
    "slug": "phys-org",
    "url": "https://phys.org/news/2026-09-room-temperature-skyrmion-based-synapses.html"
  },
  "original_language": "en",
  "account": "Researchers from the University of Edinburgh have discovered a novel method to create room-temperature skyrmion-based synapses for efficient artificial intelligence (AI) processing. Skyrmions are small, stable magnetic structures that could revolutionize memory and computing technologies by mimicking the energy-efficient neural networks of the human brain. Conventional computers waste a lot of energy by transferring information between separate processing and memory units, while neuromorphic computing aims to bridge this gap by developing electronic devices that behave more like biological neural networks. The team used a 2D ferromagnetic material called Fe₃GaTe₂ to demonstrate how collective transformations of skyrmions into stripe-like magnetic domains can create reliable artificial synapses. By controlling the duration of electrical pulses, the researchers can tune the synaptic weight and enable multiply-accumulate operations essential for neural networks. Remarkably, this new synapse operates at room temperature, addressing a major challenge in applying quantum and magnetic phenomena to practical technologies. The energy consumption of the device is estimated at approximately 0.66 picojoules per operation, comparable to other emerging memristive technologies. In a demonstration of the synapse's potential, the researchers integrated its characteristics into a neural network designed to recognize handwritten digits, achieving 96.1% accuracy. The study's lead author, Dr. Elton Santos, highlights that by exploiting collective magnetic transformations instead of manipulating individual skyrmions, the approach offers a more deterministic and reproducible way to control information, maintaining the benefits of skyrmions' small size and topological stability. This breakthrough could lead to scalable and robust spin-based computing technologies, ultimately paving the way for AI systems that are both more energy-efficient and capable of processing information collectively.",
  "summary": "Artificial intelligence is transforming how information is generated, processed and stored, but its rapid expansion is also driving unprecedented demand for computing power and electricity. Developing hardware that can process information more efficiently is therefore becoming one of the major technological challenges of the AI era.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}