Accelerating Chamber-to-Chamber Matching Using AI

A critical challenge in the high-yield manufacturing of semiconductor devices is deploying a developed manufacturing process across multiple chambers, tools, or plants to scale up production. Due to inherent tool variability, the same process settings can lead to variations in process outcomes across chambers or tools. Currently, chamber or tool matching is commonly performed experimentally, leading to considerable additional deployment costs. The following case study showcases an AI- based methodology to accelerate chamber variability and yield optimization during typical semiconductor manufacturing workflows. An optimal set of process conditions that maximize across- wafer yield and meet recipe targets is first identified for a baseline chamber. It is then shown how to match the recipe to additional chambers, accounting for variability between them and ensuring consistent process outcomes. The presented methodology requires minimal experimental effort, thereby greatly reducing process development time and cost. The results illustrate the application of this methodology across four distinct processing chambers, underscoring its versatility and efficacy.