Researchers have demonstrated how point defects in two-dimensional materials, specifically WSe2 field-effect transistors (FETs), can serve as stochastic encoders for neuromorphic computing. While defects are typically seen as detrimental to device performance, this study highlights their potential for enabling noise-resilient information processing. The team leveraged random telegraph noise (RTN) caused by defects to encode noisy medical images into stochastic spike trains, improving inference accuracy in spiking neural networks. This approach not only utilizes the unique properties of WSe2 defects but also underscores their applications in biomedical imaging and hardware acceleration for noise-tolerant neural networks, showcasing a novel computational paradigm inspired by biological systems.
For more details, please continue reading the full article under the following link:
https://www.nature.com/articles/s41467-024-54283-1
In general, if you enjoy reading this kind of scientific news articles, I would also be keen to connect with fellow researchers based on common research interests in materials science, including the possibility to discuss about any potential interest in the Materials Square cloud-based online platform ( www.matsq.com ), designed for streamlining the execution of materials and molecular atomistic simulations!
Best regards,
Dr. Gabriele Mogni
Technical Consultant and EU Representative
Virtual Lab Inc., the parent company of the Materials Square platform
Website: Home | Virtual Lab Inc.
Email: gabriele@simulation.re.kr
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