University of California, Riverside (UCR) researchers have demonstrated pattern recognition using a magnonic holographic memory device, a breakthrough they say could improve speech- and image-recognition hardware.
The researchers say the technology is unique because the input patterns are encoded into the phrases of the input spin waves, which are collective oscillations of spins in magnetic materials.
Spin-wave devices can be an improvement over their optical counterparts because they are more scalable due to their shorter wavelength. In addition, spin-wave devices are compatible with conventional electronic devices and can be integrated within a chip.
The researchers built a prototype eight-terminal device consisting of a magnetic matrix with micro-antennas to excite and detect the spin waves. The researchers collected experimental data for several magnonic matrixes and found unique output signatures corresponding to specific phase patterns. The micro-antennas enable the researchers to generate and identify any input phase pattern, a major improvement over conventional techniques.
The new approach also features the appealing property of having all of the input ports operating in parallel, which means magnonic holographic devices have the potential to be fundamentally more efficient than conventional digital circuits.
"Now, the device works not only as a memory but also a logic element," says UCR professor Alex Khitun.
From UCR Today
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