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Abstract Unique self-assembled germanium structures known as Germanium-on-Nothing (GON), which are fabricated via annealing, have buried multiscale cavities with different morphologies. Due to their unique sub-surface morphologies, GON structures are utilized in various applications including optoelectronics, micro-/nanoelectronics, and precision sensors. Each application requires different cavity shapes, and a simulation tool is able to determine the required annealing duration for a given shape. However, a theoretical simulation inevitably requires simplifications which limit its accuracy. Herein, to resolve such dependence on simplification, we introduce a deep learning-based method for simulating the transformation of sub-surface morhpology of GON over annealing. Namely, a deep learning model is trained to predict GON’s morphological transformation from 4 cross-sectional images acquired at different annealing times. Compared to conventional simulation schemes, our proposed deep learning-based simulation method is not only computationally efficient ( $$\sim 10$$ ∼ 10 min) but also physically accurate with its use of empirical data. |
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