Publications by authors named "D Grundler"
The transition from planar (2D) to three-dimensional (3D) magnetic nanostructures represents a significant advancement in both fundamental research and practical applications, offering vast potential for next-generation technologies like ultrahigh-density storage, memory, logic, and neuromorphic computing. Despite being a relatively new field, the emergence of 3D nanomagnetism presents numerous opportunities for innovation, prompting the creation of a comprehensive roadmap by leading international researchers. This roadmap aims to facilitate collaboration and interdisciplinary dialogue to address challenges in materials science, physics, engineering, and computing.
View Article and Find Full Text PDFNonlinearity of dynamic systems plays a key role in neuromorphic computing, which is expected to reduce the ever-increasing power consumption of machine learning and artificial intelligence applications. For spin waves (magnons), nonlinearity combined with phase coherence is the basis of phenomena like Bose-Einstein condensation, frequency combs, and pattern recognition in neuromorphic computing. Yet, the broadband electrical detection of these phenomena with high-frequency resolution remains a challenge.
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- Spin waves, or magnons, hold potential for advancing neuromorphic computing by addressing limitations of traditional electronic systems and architectures.
- The study investigates nonvolatile encoding of magnon signals by manipulating the magnetization of variously structured NiFe nanostripes, revealing that closely configured stripes can effectively switch magnetization at low power levels.
- The findings expand the possibilities for magnon-induced magnetization reversal on yttrium iron garnet (YIG), which is crucial for developing efficient in-memory computing technologies that utilize ultrashort magnons with minimal energy use.
Article Synopsis
- The field of magnonics focuses on utilizing collective spin excitations in magnetically ordered materials to innovate information technologies, sensing applications, and advanced computing.
- Spin waves (or magnons) allow for high-frequency data processing without the energy loss associated with moving electric charges, promising efficient alternatives to conventional processors.
- The 2024 Magnonics Roadmap outlines recent progress, future challenges, and growing interest in hybrid structures, emphasizing the potential for energy-efficient technologies as demand for machine learning and AI continues to rise.
Magnetic bit writing by short-wave magnons without conversion to the electrical domain is expected to be a game-changer for in-memory computing architectures. Recently, the reversal of nanomagnets by propagating magnons was demonstrated. However, experiments have not yet explored different wavelengths and the nonlinear excitation regime of magnons required for computational tasks.
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