  This paper demonstrates the learning of the underlying device physics by
mapping device structure images to their corresponding Current-Voltage (IV)
characteristics using a novel framework based on variational autoencoders
(VAE). Since VAE is used, domain expertise is not required and the framework
can be quickly deployed on any new device and measurement. This is expected to
be useful in the compact modeling of novel devices when only device
cross-sectional images and electrical characteristics are available (e.g. novel
emerging memory). Technology Computer-Aided Design (TCAD) generated and
hand-drawn Metal-Oxide-Semiconductor (MOS) device images and noisy
drain-current-gate-voltage curves (IDVG) are used for the demonstration. The
framework is formed by stacking two VAEs (one for image manifold learning and
one for IDVG manifold learning) which communicate with each other through the
latent variables. Five independent variables with different strengths are used.
It is shown that it can perform inverse design (generate a design structure for
a given IDVG) and forward prediction (predict IDVG for a given structure image,
which can be used for compact modeling if the image is treated as device
parameters) successfully. Since manifold learning is used, the machine is shown
to be robust against noise in the inputs (i.e. using hand-drawn images and
noisy IDVG curves) and not confused by weak and irrelevant independent
variables.
