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Design-aware virtual metrology and process recipe recommendations

Modern semiconductor fabrication employs large-scale, cutting-edge processing techniques that push the limits of chemistry and physics. While fab expansion and capital expenditures continue to grow, the human element has become ever more demanding and prone to error. To assist with this issue, computer-aided process engineering, process control, and tool monitoring will continue to rise in the coming years.

In this paper, we present an APC-integrated, customizable solution to an in-fab processing segment. Through machine learning, we combine information from design-specific extracted features with processing and metrology data to predict oxide deposition thickness. The result is a design-aware augmentation for current metrology that can recommend accurate process recipe conditions for new layouts. We also present experimental results highlighting the benefits of adding design-aware features with in-fab data to anchor and support each other across layouts and technologies. This result paves the way to decouple, isolate, and quantify the individual influences each processing step imposes on different designs at various stages of the fabrication flow.