Semiconductor process development represents a tremendous opportunity for AI-based approaches which excel in automating routine tasks and recognizing patterns in data. With the right toolset, process engineers can leverage AI models in their day-to-day development. Nevertheless, several key technical challenges must be tackled to successfully implement AI in a semiconductor fabrication environment. For example, model requirements and desired learnings are vastly different when considering the needs of process engineers performing R&D, technology ramp, or high-volume manufacturing. Moreover, preservation of data security remains a pressing issue. Most AI models rely on large sets of data which cannot be shared between manufacturers. In this talk, we will review SandBox’s key innovations in these areas. We will cover critical applications for patterning, etch, and deposition unit process optimization and new opportunities for process co-optimization.
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