Stochastic Memristive Synapses from Spin-Transfer Torque Magnetic Tunnel Junctions

Recent progress lead spin-transfer torque magnetic tunnel junctions (STT-MTJs) to emerge as a breakthrough for embedded and standalone non-volatile memory. A peculiarity of this technology, however, is its stochastic switching nature. In memory applications, the randomness of the delay to program from a memory state to another requires designing programming times with high safety margins, to ensure reliable programming. However, considering this MTJs behavior as an intrinsic feature and not as a drawback can benefit unconventional applications. In particular, here we propose to use MTJs as “stochastic binary memristive synapses”. We show how MTJs may then be used in a brain-inspired (neuromorphic) system for practical applications, where MTJs implement at the same time massive nonvolatile memory and a stochastic learning rule. We perform simulations of our system using analytical equations to describe MTJs' probabilistic behavior, in different programming regimes. These simulations highlight the potential of the technology for learning systems that are also robust to device variations. We study the impact of the different programming regimes on the operation and on the energy consumption of the system. These results open the way for unexplored applications of MTJs in robust, low power, cognitive-type systems.