  Nanoscale resistive switching devices (memristive devices or memristors) have
been studied for a number of applications ranging from non-volatile memory,
logic to neuromorphic systems. However a major challenge is to address the
potentially large variations in space and in time in these nanoscale devices.
Here we show that in metal-filament based memristive devices the switching can
be fully stochastic. While individual switching events are random, the
distribution and probability of switching can be well predicted and controlled.
Rather than trying to force high switching probabilities using excessive
voltage or time, the inherent stochastic nature of resistive switching allows
these binary devices to be used as building blocks for novel error-tolerant
computing schemes such as stochastic computing and provide a needed "analog"
feature in neuromorphic applications. To verify such potential, we demonstrated
memristor-based stochastic bitstreams in both time and space domains, and show
that an array of binary memristors can act as a multi-level "analog" device for
neuromorphic applications.
