Technical cleanliness is part of the ZEN core software portfolio from the industrial microscopy sector. To optimize particle classification, ZEISS now offers ZEISS ZEN core users the Technical Cleanliness Analysis („TCA“) solution with three pre-trained modules based on machine learning. This module offers the functionality to classify particles based on measured parameters of an analyzed image. Samples are analyzed via three pre-trained machine learning models which can be additionally trained by users & customers. Samples are analyzed via gray scale determination and the pre-trained Machine Learning model for object classification runs in the background in the new TCA job templates, while the type classification is checked in parallel, based on the existing particle measurement results. This additional check of the type classification via machine learning algorithms „looks at” the particle results obtained with the classical analysis for size, shape, intensity and type classification, among others, and combines the various features into a large number of uncorrelated decision trees. The results are evaluated individually for each decision tree using the classification trained to the object classification model. The result is an automatic prediction of the particle type.

Itemize particles by quantity, size distribution and morphology, and distinguish metallic from non-metallic particles and fibers down to 2 μm. Create cleanliness reports according to industry standards.

Characterize process-critical particles and identify critical particlesusing correlative microscopy, which combines your data from both light and electron microscopes in a single workflow.

Measure the morphological properties of particles and use fully automated elemental analysis to classify particles by chemical composition. ZEISS SmartPI, the particle analysis software for electron microscopes, automates the detection, analysis, and classification of particles, combining microscope control, image processing and elemental analysis into a single application.