Computational tool allows for the development of next-generation communications devices
UCLA Samueli engineers have developed a new tool to model how magnetic materials, which are used in smartphones and other communications devices, interact with incoming radio signals that carry data. It accurately predicts these interactions down to the nanometer scales required to build state-of-the-art communications technologies.
The tool allows engineers to design new classes of radio frequency-based components that are able to transport large amounts of data more rapidly, and with less noise interference. Future use cases include smartphones to implantable health monitoring devices.
Magnetic materials can attract or repel each other based on their polar orientation–positive and negative ends attract each other, while two positives or two negatives repel. When an electromagnetic signal like a radio wave passes through such materials, a magnetic material acts as a gatekeeper, letting in the signals that are desired, but keeping out others. They can also amplify the signal, or dampen the speed and strength of the signal.
Engineers have used these gatekeeper-like effects, called “wave-material interactions,” to make devices used in communications technologies for decades. For example, these include circulators that send signals in specific directions or frequency-selective limiters that reduce noise by suppressing the strength of unwanted signals.
Current design tools are not comprehensive and precise enough to capture the complete picture of magnetism in dynamic systems, such as implantable devices. The tools also have limits in the design of consumer electronics.
“Our new computational tool addresses these problems by giving electronics designers a clear path toward figuring out how potential materials would be best used in communications devices,” said Yuanxun “Ethan” Wang, a professor of electrical and computer engineering who led the research. “Plug in the characteristics of the wave and the magnetic material, and users can easily model nanoscale effects quickly and accurately. To our knowledge, this set of models is the first to incorporate all the critical physics necessary to predict dynamic behavior.”
thumbnail courtesy of ucla.edu