The Modeling of Post-Annealing and Etching Processes of ALD SiO₂ Using Intermediate Variables Considering Digital Twin Model Reusability

In this study, we examined a digital twin model that has multiple processes. Generally, previous processes affect subsequent processes in the semiconductor manufacturing process. Therefore, to construct reusable modular models, the mutual influences between processes should be defined and concisely represented. We built a digital twin model involving the post-annealing and wet etching of an oxide film formed by atomic layer deposition (ALD), as a case study. We developed a modular model that separated processes based on intermediate variables extracted through physical analysis. The high coefficient of determination obtained from the prediction results suggests that these intermediate variables sufficiently captured the effect of the preceding processes. Further, we explored concepts for improving model reusability using class structure analysis within an object-oriented programming (OOP) framework. We observed the need for encapsulating physics-based intermediate variables within appropriate classes to separate process- and device-specific descriptions. The encapsulated intermediate variables indirectly represented process influence and enabled the modularization of class-internal models. These findings help in reducing dependencies between models, thereby contributing to improved model reusability.