Machine-learning-based atom probe crystallographic analysis

Atom probe tomography is known for its accurate compositional analysis at the nanoscale. However, the patterns created by successive hits on the single particle detector during experiments often contain complementary information about the specimen's crystallography, including structure and orientation. This information remains in most cases unexploited because it is, up to now, retrieved predominantly manually. Here, we propose an approach combining image analysis techniques for feature selection and deep-learning to automatically interpret the patterns. Application of unsupervised machine learning techniques allows to build and train a deep neural network, based on a library generated from theoretically known crystallographic angular relationships. This approach enables direct interpretation of the detector hit maps, as shown here on the example of numerous pure-Al, and is robust enough to function under various conditions of base temperature, pulsing mode and pulse fraction. We benchmark our approach against recent attempts to automate the pattern identification via Hough-transform and discuss the current limitations of our approach. This new automated approach renders crystallographic atom probe tomography analysis more efficient, feature-sensitive, robust, user-independent and reliable. With that, deep-learning algorithms show a great potential to give access to combined atom probe crystallographic and compositional analysis to a large community of users.