Proceedings Article | 28 April 2017
KEYWORDS: Metrology, Data fusion, Process control, Atmospheric modeling, Nanotechnology, Chemistry, Semiconductors, Process modeling, Crystals, Industrial chemicals
The development and integration of new materials and structures at the nanoscale require multiple parallel characterizations in order to control mostly physico-chemical properties as a function of applications. Among all properties, we can list physical properties such as: size, shape, specific surface area, aspect ratio, agglomeration/aggregation state, size distribution, surface morphology/topography, structure (including crystallinity and defect structure), solubility and chemical properties such as: structural formula/molecular structure, composition (including degree of purity, known impurities or additives), phase identity, surface chemistry (composition, charge, tension, reactive sites, physical structure, photocatalytic properties, zeta potential), hydrophilicity/lipophilicity. Depending on the final material formulation (aerosol, powder, nanostructuration…) and the industrial application (semiconductor, cosmetics, chemistry, automotive…), a fleet of complementary characterization equipments must be used in synergy for accurate process tuning and high production yield. The synergy between equipment so-called hybrid metrology consists in using the strength of each technique in order to reduce the global uncertainty for better and faster process control. The only way to succeed doing this exercise is to use data fusion methodology.
In this paper, we will introduce the work that has been done to create the first generic hybrid metrology software platform dedicated to nanotechnologies process control. The first part will be dedicated to process flow modeling that is related to a fleet of metrology tools. The second part will introduce the concept of entity model which describes the various parameters that have to be extracted. The entity model is fed with data analysis as a function of the application (automatic analysis or semi-automated analysis). The final part will introduce two ways of doing data fusion on real data coming from imaging (SEM, TEM, AFM) and non-imaging techniques (SAXS). First approach is dedicated to high level fusion which is the art of combining various populations of results from homogeneous or heterogeneous tools, taking into account precision and repeatability of each of them to obtain a new more accurate result. The second approach is dedicated to deep level fusion which is the art of combining raw data from various tools in order to create a new raw data. We will introduce a new concept of virtual tool creator based on deep level fusion. As a conclusion we will discuss the implementation of hybrid metrology in semiconductor environment for advanced process control
