During data-driven process condition optimization on
a laboratory
scale, only a small-size data set is accessible and should be effectively
utilized. On the other hand, during process development, new operations
are frequently inserted or current operations are modified. These
accessible data sets are somewhat related but not exactly the same
type. In this study, we focus on the prediction of the quality of
the interface between an insulator and GaN as a semiconductor for
the potential application of GaN power semiconductor devices. The
quality of the interface was represented as the interface state density, D
it, and the inserted operation to the process
was the ultraviolet (UV)/O3-gas treatment. Our retrospective
evaluation of model-building approaches for D
it prediction from a process condition revealed that for the
UV/O3-treated interfaces, data of interfaces without the
treatment contributed to performance improvement. Such performance
improvement was not observed when using a data set of Si as the semiconductor.
As a modeling method, the automatic relevance vector-based Gaussian
process regression with the prior distribution of the length-scale
parameters exhibited a relatively high predictive performance and
represented a reasonable uncertainty of prediction as reflected by
the distance to the training data set. This feature is a prerequisite
for a potential application of Bayesian optimization. Furthermore,
hyperparameters in the prior distribution of the length-scales could
be optimized by leave-one-out cross-validation.
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