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
.
With the shrinking of the IC technology node, optical proximity effects (OPC) and etch proximity effects (EPC) are the two major tasks in advanced photolithography patterning. Machine learning has emerged in OPC/EPC problems because conventional optical-solver-based OPC is time-consuming, and there is no physical model existing for EPC. In this work, we use dimensionality reduction (DR) algorithms to reduce the computation time of complex OPC/EPC problems while the prediction accuracy is maintained. Also, we implement a pure machine learning approach where the input masks are directly mapped to the output etched patterns. While one photolithographic mask can generate many experimental patterns at once, our pure ML-based approach can shorten the trial-and-error period in the photolithographic correction. Additionally, we demonstrate the automation in SEM images preprocessing using feature detection, and this facilitates intelligent manufacturing in semiconductor processing. The input vector dimensions are effectively reduced by two orders of magnitude while the observed mean squared error is not affected significantly. The computation runtime is reduced from 4804 s of the baseline calculation to 10 s-200 s The MSE values changed from the baseline 0.037 to 0.037 for singular value decomposition (SVD), to 0.039 for independent component analysis (ICA), and to 0.035 for factor analysis (FA).
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