  The modelling of memristive devices is an essential part of the development
of novel in-memory computing systems. Models are needed to enable the accurate
and efficient simulation of memristor device characteristics, for purposes of
testing the performance of the devices or the feasibility of their use in
future neuromorphic and in-memory computing architectures. The consideration of
memristor non-idealities is an essential part of any modelling approach. The
nature of the deviation of memristive devices from their initial state,
particularly at ambient temperature and in the absence of a stimulating
voltage, is of key interest, as it dictates their reliability as information
storage media - a property that is of importance for both traditional storage
and neuromorphic applications. In this paper, we investigate the use of a
generative modelling approach for the simulation of the delay and initial
resistance-conditioned resistive drift distribution of memristive devices. We
introduce a data normalisation scheme and a novel training technique to enable
the generative model to be conditioned on the continuous inputs. The proposed
generative modelling approach is suited for use in end-to-end training and
device modelling scenarios, including learned data storage applications, due to
its simulation efficiency and differentiability.
