Abstract
Reservoir computing is a neural network algorithm that reduces the training needed for a neural network to be function. Recently, reservoir computing has been implemented using MEMs devices with prevalent non-linear dynamics to perform non-linear processing tasks. While partially explored in the past, there has been renewed interest in using Surface Acoustic Wave devices as low energy radio-frequency processors. However they have yet to be explored in the reservoir computing framework. In this work, a 39.16 MHz two-port SAW resonator on chemically reduced YZ Lithium Niobate is design and measured. The quality factor, insertion loss, linear transmission, and non-linear transmission of the devices is measured, and the relationship of these properties to reservoir computing is discussed. The SAW resonator is then configured as a time-multiplexed reservoir, and it’s non-linear processing capabilities are discussed using the time-delayed parity benchmark.
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