It is always highly
desired to have a well-defined relationship
between the chemistry in semiconductor processing and the device characteristics.
With the shrinkage of technology nodes in the semiconductors roadmap,
it becomes more complicated to understand the relation between the
device electrical characteristics and the process parameters such
as oxidation and wafer cleaning procedures. In this work, we use a
novel machine learning approach, i.e., physics-assisted multitask
and transfer learning, to construct a relationship between the process
conditions and the device capacitance voltage curves. While conventional
semiconductor processes and device modeling are based on a physical
model, recently, the machine learning-based approach or hybrid approaches
have drawn significant attention. In general, a huge amount of data
is required to train a machine learning model. Since producing data
in the semiconductor industry is not an easy task, physics-assisted
artificial intelligence has become an obvious choice to resolve these
issues. The predicted
C
–
V
uses the hybridization of physics, and machine learning provides
improvement while the coefficient of determination (
R
2
) is 0.9442 for semisupervised multitask learning (SS-MTL)
and 0.9253 for transfer learning (TL), referenced to 0.6108 in the
pure machine learning model using multilayer perceptrons. The machine
learning architecture used in this work is capable of handling data
sparsity and promotes the usage of advanced algorithms to model the
relationship between complex chemical reactions in semiconductor manufacturing
and actual device characteristics. The code is available at
.
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