  Traditional TCAD simulation has succeeded in predicting and optimizing the
device performance; however, it still faces a massive challenge - a high
computational cost. There have been many attempts to replace TCAD with deep
learning, but it has not yet been completely replaced. This paper presents a
novel algorithm restructuring the traditional TCAD system. The proposed
algorithm predicts three-dimensional (3-D) TCAD simulation in real-time while
capturing a variance, enables deep learning and TCAD to complement each other,
and fully resolves convergence errors.
