KEYWORDS: Semiconducting wafers, Etching, Education and training, Chemical mechanical planarization, Metrology, Machine learning, Data modeling, Process control, Time metrology, Visibility
Optical Critical Dimension (OCD) spectroscopy is a reliable, non-destructive, and high-throughput measurement technique for metrology and process control that is widely used in semiconductor fabrication facilities (fabs). Wafers are sampled sparsely in-line, and measured at about 10-20 predetermined locations, to extract geometrical parameters of interest. Traditionally, these parameters were deduced by solving Maxwell’s equations for the specific film stack geometry. Recently advanced machine learning (ML) models, or combinations of ML and geometric models, has become increasingly attractive due to the several advantages of this approach. Advanced node processes can benefit from more extensive data sampling, but this conflicts with measurement cycle time goals and overall metrology tool costs, which cause fabs to use sparse sampling schemes. In this paper, we introduce a novel methodology that allows wafers to be sampled sparsely but provides the parameters of interest as if they were densely measured. We show how such a methodology allows us to increase data output with no impact on overall measurement time, while maintaining high accuracy and robustness. Such a capability has potentially far-reaching implications for improved process control and faster yield learning in semiconductor process development.
Device scaling has not only driven the use of measurements on more complex structures, in terms of geometry, materials, and tighter ground rules, but also the need to move away from non-patterned measurement sites to patterned ones. This is especially of concern for very thin film layers that have a high thickness dependence on structure geometry or wafer pattern factor. Although 2-dimensional (2D) sites are often found to be sufficient for process monitoring and control of very thin films, sometimes 3D sites are required to further simulate structures within the device. The measurement of film thicknesses only a few atoms thick on complex 3D sites, however, are very challenging. Apart from measuring thin films on 3D sites, there is also a critical need to measure parameters on 3D sites, which are weak and less sensitive for OCD (Optical Critical Dimension) metrology, with high accuracy and precision. Thus, state-ofthe-art methods are needed to address such metrology challenges. This work introduces the concept of Enhanced OCD which uses various methods to improve the sensitivity and reduce correlations for weak parameters in a complex measurement. This work also describes how more channels of information, when used correctly, can improve the precision and accuracy of weak, non-sensitive or complex parameters of interest.
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