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.
Keywords: OCD metrology, machine learning, WiW, wafer map, sampling scheme.