This paper introduces the concept of Digital Factory (DF) that can be used for representing the technological capabilities of manufacturing facilities. The DF, as the digital twin of physical facilities, replicates the facility in terms of installed machinery, material handling equipment, and layout. DF is supported by a formal ontology that describes the capabilities of the factory in a formal and machine-understandable fashion and enables capability quantification and visualization. Through exploring and querying the Digital Factories, companies can develop a deeper and more precise understanding of the technological capabilities of prospective suppliers, thus making more informed decisions when building supply chain partnerships. By creating their digital twins, small to medium-sized manufacturers can significantly improve their visibility in the virtual space. Digital Factories also enable automated supply chain formation. DF is introduced and discussed from a conceptual perspective in this paper. Also, a mathematical model for quantifying the processing capabilities of Digital Factories is introduced.

It was recently demonstrated that scatterometry-based metrology has the capability to perform high-throughput metrology on large-area nanopatterned surfaces. However, the way this approach is currently pursued requires an a priori generated library of reflectance spectra to be simulated for an exhaustive set of possible underlying critical dimensions (CDs) characterizing the measured nanopatterns. Generating this library is time consuming and can be infeasible for complex patterns characterized by a large number of CDs. This article addresses the aforementioned drawback of optical inspection of CDs of nanopatterned surfaces through the use of an inverse problem-based optimization methodology coupled with a recently introduced approach for efficient organization of the library of previously simulated reflectance spectra. Specifically, for each physically measured reflectance spectrum, the best matching simulated spectrum is sought in the initial incomplete library in order to serve as the initial guess for the inverse problem optimization process. Through that optimization process, further refinements of the best matching simulated spectra are conducted to obtain sufficiently accurate estimates of the CDs characterizing the inspected nanopattern geometries. Capabilities of the newly proposed approach are evaluated through inspection of semiconductor wafer samples with hourglass patterns characterized by eight CDs. It was observed that one can obtain significantly faster measurements of CDs compared to inspection times associated with scanning electron microscopy, while at the same time not deteriorating the corresponding Gage Repeatability and Reproducibility. In conclusion, this method enables real-time, accurate, and repeatable metrology of CDs of large-area nanostructured surfaces with complex nanopatterns.