While digital integrated circuits with von Neumann architectures, having exponentially evolved for half a century, are an indispensable building block of today's information society, recently growing demand on executing more complex tasks like the human brain has allowed a revisit to the architecture of information processing. Brain-inspired hardware using artificial neural networks is expected to offer a complementary approach to deal with complex problems. Since the neuron and synapse are key components of brains, most of the mathematical models of artificial neural networks require artificial neurons and synapses. Consequently, much effort has been devoted to creating artificial neurons and synapses using various solid-state systems with ferroelectric materials, phase-change materials, oxide-based memristive materials, and so on. Here, we review an example of studies on an artificial synapse based on spintronics and its application to artificial neural networks. The spintronic synapse, having analog and nonvolatile memory functionality, consists of an antiferromagnet/ferromagnet heterostructure and is operated by spin-orbit torque. After giving an overview of this field, we describe the operation principle and results of analog magnetization switching of the spintronic synapse. We then review a proof-of-concept demonstration of the artificial neural network with 36 spintronic synapses, where an associative memory operation based on the Hopfield model is performed and the learning ability of the spintronic synapses is confirmed, showing promise for low-power neuromorphic computation.
ASJC Scopus subject areas
- Physics and Astronomy(all)