Hybrid Metrology for 3D Architectures Using Machine Learning

The march towards miniaturization of semiconductor devices places strong constraints on metrology techniques. Effective process control for current and next-gen device manufacture demands characterization of complex three-dimensional structures, accurately, quickly, and preferably non-destructively. In isolation, no currently existing metrology technique can meet all these challenges. We present a hybrid metrology solution in the form of a Physics-Enabled AI system, based on the commercial software tool SandBox Studio AI. Through the integration of information from diverse metrology sources, the system adeptly generates detailed, high-fidelity 3D reconstructions and allows for the extraction of measurements from various planes within the structure, while minimizing measurement-related expenses and material waste. The method's efficacy was demonstrated on two 3D structures: Gate-All-Around (GAA) FET and 3D NAND Slit, achieving sub-nm accuracy even with limited metrology input data.