Artificial intelligence with radio-frequency spintronic devices

For numerous Radio-Frequency applications such as medicine, RF fingerprinting or radar classification, it is important to be able to apply Artificial Neural Network on RF signals. In this work we show that it is possible to apply directly Multiply-And-Accumulate operations on RF signals without digitalization, thanks to Magnetic Tunnel Junctions (MTJs). These devices are similar to the magnetic memories already industrialized and compatible with CMOS. We show experimentally that a chain of these MTJs can rectify simultaneously different RF signals, and that the synaptic weight encoded by each junction can be tune with their resonance frequency. Through simulations we train a layer of these junctions to solve a handwritten digit dataset. Finally, we show that our system can scale to multi-layer neural networks using MTJs to emulate neurons. Our proposition is a fast and compact system that allows to receive and process RF signals in situ and at the nanoscale.