Extended Natural Dataset for Rapid Deep-Learning-Based Optical Measurement of Printed Linear Structures

However, they require sophisticated and expensive equipment as well as increased personnel qualification. While the integration of machine learning contributes to alleviating these requirements, it needs a time-consuming and tedious preparation of training datasets.

We introduce a twin concept to conduct rapid measurement of various geometrical properties of printed lines using deep learning. For the first time, a tedious collection and assessment of training data is substituted by synthetic generation of digital samples, which enables flexible, fast, and precise data processing. A full-field analysis of a non-preprocessed image conducted by a neural network facilitates measurement of multiple geometrical properties of an object at once.