A Digital Twin Framework With Deep Feature Extraction and Gaussian Process for Multi-Objective Optimization in Semiconductor Manufacturing

Achieving optimal parameter settings in epitaxial silicon carbide (Epi SiC) manufacturing is challenging due to the need to simultaneously meet conflicting multi-objective requirements, such as precise thickness control and uniform doping. This paper presents an extended Digital Twin (DT) framework that incorporates the Multi-Objective Optimization with Deep-Feature Gaussian Process (MOODFG) algorithm, explicitly extending our prior DT-in-the-loop framework (MRBORI) in [26] from single-objective tuning to spec-driven multi-objective recipe optimization. The framework enables the effective optimization of high-dimensional and interdependent process parameters, overcoming limitations of traditional methods. The key innovation of the MOODFG algorithm lies in its integration of deep feature extraction and Gaussian Process Regression, which allows dynamic refinement of feature representations and surrogate models through iterative learning. Unlike MRBORI [26] (one objective per run), MOODFG minimizes a weighted distance to a user-specified target vector (e.g., thickness and doping) and provides an uncertainty-aware target-attainment certificate with a practical stopping rule for deployment. This approach ensures robust convergence to optimal parameter values while efficiently balancing multiple objectives. Experimental validation using real-world Epi SiC manufacturing data demonstrates significant improvements in yield, parameter stability, and process adaptability, highlighting the framework’s transformative potential for semiconductor manufacturing.