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Dual beam focused ion beam/scanning electron microscopy (FIB/SEM) is a critical characterization technique that is used as inline metrology from early stages of process developments until high volume manufacturing (HVM) of magnetic read/write heads in hard disk drive (HDD) due to the complex three-dimensional geometry [1]. Despite its destructive nature, FIB/SEM metrology is critical to support high throughout manufacture process for advanced process control during HVM in HDD industry. Final cross-sectional SEM images typically include several CD measurements and embedded or standalone standard machine vision applications are used as part of the metrology process. However, these applications are typically not able to accommodate various process changes during the rapid process development, and manual engineer assistance are often needed for the accurate cut placement and SEM search. On the other hand, optimization of machine vision application typically requires a reasonable number of images to allow training and optimization of edge finder and pattern recognition functions. Reducing the training and optimization time needed for machine vision applications reduces the learning time during new process development. In this work, we are introducing a machine learning based metrology application that minimizes the need for engineer involvement for recipe optimization during the rapid process development [2]. By addition of the process margin entities to the machine learning model, the recipe robustness is significantly improved at the time of transition to new product introduction (NPI) and high volume manufacturing (HVM). We compare the new machine learning based metrology application against the legacy machine vision application and study its impact on recipe writing time, wafer to wafer variations, and total measurement uncertainty (TMU). The new application allows recipes capable of cross-design metrology.
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