  Memristors are non-volatile nano-resistors. Their resistance can be tuned by
applied currents or voltages and set to a large number of levels between two
limit values. Thanks to these properties, memristors are ideal building blocks
for a number of applications such as multilevel non-volatile memories and
artificial nano-synapses, which are the focus of this work. A key point towards
the development of large scale memristive neuromorphic hardware is to build
these neural networks with a memristor technology compatible with the best
candidates for the future mainstream non-volatile memories. Here we show the
first experimental achievement of a memristor compatible with Spin-Torque
Magnetic Random Access Memory. The resistive switching in our spin-torque
memristor is linked to the displacement of a magnetic domain wall by
spin-torques in a perpendicularly magnetized magnetic tunnel junction. We
demonstrate that our magnetic synapse has a large number of intermediate
resistance states, sufficient for neural computation. Moreover, we show that
engineering the device geometry allows leveraging the most efficient spin
torque to displace the magnetic domain wall at low current densities and thus
to minimize the energy cost of our memristor. Our results pave the way for
spin-torque based analog magnetic neural computation.
