Dependence-Analysis-Based Data-Refinement in Optical Scatterometry for Fast Nanostructure Reconstruction

Abstract

Optical scatterometry is known as a powerful tool for nanostructure reconstruction due to its advantages of being non-contact, non-destructive, low cost, and easy to integrate. As a typical model-based method, it usually makes use of abundant measured data for structural profile reconstruction, on the other hand, too much redundant information significantly degrades the efficiency in profile reconstruction. We propose a method based on dependence analysis to identify and then eliminate the measurement configurations with redundant information. Our experiments demonstrated the capability of the proposed method in an optimized selection of a subset of measurement wavelengths that contained sufficient information for profile reconstruction and strikingly improved the profile reconstruction efficiency without sacrificing accuracy, compared with the primitive approach, by making use of the whole spectrum.

Condition-number-based measurement configuration optimization for nanostructure reconstruction by optical scatterometry

“…In particular, we explored the range of incidence angles θ from 60 • to 70 • with an increment of 1 • increment, and azimuthal angles φ from 0 • to 90 • with an increment of 1 • . Compared to previous works [8,[14][15][16], the implementation of this surrogate mode allows us to consider more refined angular step sizes during the optimization process, leading to improved accuracy in identifying optimal configurations. As can be seen from figure 6, the measurement configurations with smaller condition numbers (indicated by the purple region) are distributed within the ranges of 60 • ⩽ θ ⩽ 70 • and 20 • ⩽ φ ⩽ 80 • , as well as within the ranges of 64 • ⩽ θ ⩽ 70 • and 80 • ⩽ φ ⩽ 90 • , predicting that the accuracy of the extracted parameters under these measurement configurations is less susceptible to the errors in measured signatures.…”

“…x WJ x −1 . Several MCO methods, as mentioned in [8,[13][14][15][16], employ this coefficient matrix as a foundation of the optimization objective. In this study, we treat the MCO problem based on the condition-number-based error estimation technique.…”

“…Chen et al treated the MCO problem as a multi-objective optimization problem, incorporating both parameter variances and parameter correlations as optimization objectives, and they utilized a multiobjective genetic algorithm to tackle this problem [8]. Dong et al proposed a method to reduce the uncertainty of the parameters in Mueller matrix ellipsometer by removing the measurement configurations with redundant information based on the dependence-analysis theory [14]. Chen et al also introduced an MCO method based on the theoretical analysis of error propagation and treated the norm of the configuration error propagating matrix as a metric to evaluate the impact of configuration errors on measurement accuracy [15].…”

Optical wafer defect inspection at the 10 nm technology node and beyond

“…However, the defects reported by a standard optical defect inspection tool require a separate SEM review to determine if the defects are patterning defects [124]. Optical scatterometry [125,126], which is also referred to as optical critical dimension metrology, measures profile parameters of periodic nanostructures by leveraging the diffracted polarization properties of light on the wafer [127][128][129][130], i.e. comparing the measured change of polarization state to the simulated one.…”

Editorial on Special Issue “Modern Applications in Optics and Photonics: From Sensing and Analytics to Communication”

“…For example, Kerrouche et al [9] report the development of rapid and low-cost pathogen detection systems using microfluidic technology and optical image processing by employing a cost-effective microscopic camera and computational algorithms and detecting small size microbeads (1-5 µm) from a measured water sample. In contrast, Dong et al [10] propose a method based on dependence analysis to identify and then eliminate the measurement configurations with redundant information in optical scatterometry for fast nanostructure reconstruction. In terms of Optical Coherence Tomography (OCT), Yi et al [11] report a mesh-based Monte Carlo model in order to study OCT signals reflecting the structural and functional activities of brain tissue as well as to improve the quantitative accuracy of chromophores in tissue.…”