IEEE Pikes Peak Section

Magnons are the fundamental spin excitations of magnetically ordered materials, such as ferromagnets and antiferromagnets. Magnons combine non-linear properties with tunability and sub-micrometer wavelengths in the gigahertz-frequency regime. The field of magnonics [1] explores potential applications of magnons for information processing and communication. These applications encompass wave-based Boolean logic, neural networks, quantum technologies and miniaturized […]

Magnetic skyrmions are localized spin textures in-which the magnetic moments curl into a continuous, coplanar loop, bounded on its outside perimeter and core by moments in the out-of-plane direction. This very specific configuration provides a non-trivial topology and quasiparticle qualities, including skyrmion kinetics and dynamic modes. Hybrid skyrmions have shown the largest stability envelope, which […]

Artificial spin ice (ASI) is a highly customizable materials platform used for studying fundamental properties of interaction and frustration in magnetic systems, with potential applications in novel computing architectures. Perpendicular ASI systems are advantageous because they have isotropic interactions between magnetic elements and high perpendicular stray fields that could be used for magnetic imprinting. These […]

IEEE Magnetics Society Pikes Peak Chapter annual meeting. The questions about the future of the chapter will be discussed including the elections of the new Chair and Treasurer.

The wildly growing field of antiferromagnetic spintronics is currently addressing several fundamental questions. A major topic of investigation concerns the generation and manipulation of coherent magnons on the ultrafast timescale. The development of novel pulsed-laser sources has enabled scientists to address the following scientific question: which magnetic excited state can be induced by resonantly drive […]

Artificial intelligence is increasingly ubiquitous across tech and broader society. While incredibly powerful, the energy demands of operating deep-learning networks on traditional von Neumann computers are spiralling unsustainably - limiting scalability and presenting a barrier to zero-carbon futures[1].  A huge reason for this is that existing computing architectures look nothing like the brain, and as […]

Artificial spiking neurons are the primary building blocks in a new class of neuromorphic computer hardware, for example, Intel’s Loihi. Spiking spintronic neurons hold promise to transform conventional computer architectures for increased processing speed and computational efficiency in machine learning tasks. This talk will show that magnetic tunnel junctions have the capacity to behave as […]

Resistive switching and spintronics emerged as leading approaches for the development of scalable and energy-efficient memories and information processing devices. In resistive switching systems, an electrical stimulus, voltage or current, programs the material’s resistivity. In spintronics, electrical signals are used to manipulate and probe the material’s magnetic configuration. Combing resistive switching and spintronics functionalities in […]

Nuclear magnetic resonance (NMR) is a powerful analytical tool that allows for noninvasive and nondestructive characterization of the chemical composition of a sample. Furthermore, low field NMR (B0 < 1 T) offers many potential advantages when compared to high field NMR, such as lower costs, smaller size, portability, lower power consumption and the ability to […]

The current revolution in quantum technologies relies on coherently linking quantum objects like quantum bits (“qubits”). Coherent magnonic excitations of low-loss magnetic materials can wire together these qubits for sensing, memory, and computing. Coherent magnonics may reduce the size of superconducting qubits (which otherwise struggle with the large scale of microwave excitations) and may increase […]