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Semiconductor manufacturing requires measurements to control the size of nanoscale devices (referred to as the critical dimension: CD) within patterned layers and for monitoring the placement of new levels upon existing layers (known as overlay metrology). As device sizes continue to decrease, new approaches are required to refine these measurements. A significant body of recent research has investigated new optical technologies for CD and overlay metrology on scales of 32nm and smaller. Much of this work has focused on scatterometry and, more recently, scatterfield microscopy, a technique combining well-defined angle-resolved illumination with image-forming optics.1, 2 These optical methods are of particular interest because of their nondestructive, high-throughput characteristics and their potential for excellent sensitivity and accuracy. However, their measurement uncertainties are fundamentally limited by the underlying cross-correlations between the different fit parameters, e.g., line widths and heights.3 To reduce parametric correlation and improve measurement performance and uncertainties, we have developed a Bayesian statistical approach that integrates a priori information gleaned from other measurements. This allows us to embed information obtained from reference metrology and complimentary ellipsometry of the optical constants, or to constrain the floating parametric range based on physical limits or known manufacturing variability. We have implemented this approach using scatterfield microscopy, but it applies equally to scatterometry or methods such as scanning-electron microscopy. The scatterfield-microscopy instrument is based on a Köhler illuminated bright-field microscope, such that each point at the conjugate back focal plane maps nominally to a plane wave of illumination at the sample.4 Access to a large conjugate back Figure 1. Experimental data and library data fits for three die from the overlay-metrology advisory group OMAG-3 wafer. Top and middle critical dimensions (CDs) show good agreement with reference values.

Bayesian statistics integrates prior information in parametric optical modeling