  Memristor crossbar arrays are used in a wide range of in-memory and
neuromorphic computing applications. However, memristor devices suffer from
non-idealities that result in the variability of conductive states, making
programming them to a desired analog conductance value extremely difficult as
the device ages. In theory, memristors can be a nonlinear programmable analog
resistor with memory properties that can take infinite resistive states. In
practice, such memristors are hard to make, and in a crossbar, it is confined
to a limited set of stable conductance values. The number of conductance levels
available for a node in the crossbar is defined as the crossbar's resolution.
This paper presents a technique to improve the resolution by building a
super-resolution memristor crossbar with nodes having multiple memristors to
generate r-simplicial sequence of unique conductance values. The wider the
range and number of conductance values, the higher the crossbar's resolution.
This is particularly useful in building analog neural network (ANN) layers,
which are proven to be one of the go-to approaches for forming a neural network
layer in implementing neuromorphic computations.
