  Nanomagnets with small shape anisotropy energy barriers on the order of the
thermal energy have unstable magnetization that fluctuates randomly in time.
They have recently emerged as promising hardware platforms for stochastic
computing and machine learning because the random magnetization states can be
harnessed for probabilistic bits. Here, we have studied how the statistics of
the magnetization fluctuations (e.g. the power spectral density) is affected by
(i) moderate variations in the barrier height of the nanomagnet and (ii) the
presence of structural defects, in order to assess how robust the computing
platform is. We found that the power spectral density is relatively insensitive
to moderate barrier height change and also relatively insensitive to the
presence of small localized defects. However, extended (delocalized) defects,
such as thickness variations over a significant fraction of the nanomagnet,
affect the power spectral density very noticeably. As a result, small
variations in the shape (causing small variations in the barrier height), or
small localized defects, are relatively innocuous and tolerable, but
significant variation of the nanomagnet thickness is not. Consequently, tight
control over the nanomagnet thickness must be maintained for stochastic
computing applications.
