  Interest in Restricted Boltzmann Machine (RBM) is growing as a generative
stochastic artificial neural network to implement a novel energy-efficient
machine-learning (ML) technique. For a hardware implementation of the RBM, an
essential building block is a reliable stochastic binary neuron device that
generates random spikes following the Boltzmann distribution. Here, we propose
a highly-scalable stochastic neuron device based on Ovonic Threshold Switch
(OTS) which utilizes the random emission and capture process of traps as the
source of stochasticity. The switching probability is well described by the
Boltzmann distribution, which can be controlled by operating parameters. As a
candidate for a true random number generator (TRNG), it passes 15 among the 16
tests of the National Institute of Standards and Technology (NIST) Statistical
Test Suite (Special Publication 800-22). In addition, the recognition task of
handwritten digits (MNIST) is demonstrated using a simulated RBM network
consisting of the proposed device with a maximum recognition accuracy of 86.07
%. Furthermore, reconstruction of images is successfully demonstrated using
images contaminated with noises, resulting in images with the noise removed.
These results show the promising properties of OTS-based stochastic neuron
devices for applications in RBM systems.
