The research and development steps in the semiconductor industry require tools that are able to handle features with large variation across the images, but also tools that can reproduce the definition of an edge taught by an expert. This definition should be easily modified to mimic the expert decisions in order to reduce the time spent by process engineers during research and development phases. We developed a patterned edge model allowing to detect the profile of patterned objects in microscopic images. A complementary tool is proposed to customize the definition between two materials according to the expert targets. The obtained profiles serve as a basis to perform robust metrology and ensure quality control of the manufactured semiconductor components.
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.
KEYWORDS: 3D metrology, Machine learning, Scanning electron microscopy, 3D image processing, Process engineering, Transmission electron microscopy, Image processing, 3D modeling, Semiconductors, Computer simulations
We present a machine learning-based metrology pipeline for electron microscope imagery in the semiconductor industry. The pipeline is targeted to reduce the time spent by Process Engineers during research and development, by automating measurements of features according to their instructions in the form of a “measurement recipe”. Specifically, we present the principles and functionality of tools to measure Fin and 3D Memory structures based on edge finding algorithms, including through direct modelling of the SEM acquisition process to better capture blurred-appearing features.
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