KEYWORDS: Inverse optics, Scatterometry, Inverse problems, Optics manufacturing, Transmission electron microscopy, Error analysis, Integrated optics, Nanostructures, Process control, Chemical elements
Optical scatterometry is widely used in the process control of integrated circuits (IC) manufacturing due to its inherent
advantages such as nondestruction, high sampling rate, large aerial coverage and low-cost. However, in the conventional
inverse problem solvent of optical scatterometry, the measurement errors are usually excessively simplified as normally
distributed errors, which deviate from the actual complex ones. In this work, we will demonstrate that there exist typical
outlying measurement errors in the measurement signature, and these outlying measurement errors will notably affect the
result of each iteration step in the conventional Gauss-Newton (GN) method. By performing a method based on the
principle of least trimmed squared estimator (LTS) regression instead of each GN iteration step, the higher measurement
accuracy of a nanostructure can be achieved. The remarkably improved reconstruction of a deep-etched multilayer
grating has demonstrated the feasibility of the proposed method.
A library search is a widely used method for the reconstruction of diffraction structures in optical scatterometry. In a library search, if the actual geometrical model of a measured signature is different from the model used in the establishment of a library, the search result will be meaningless. Therefore, the identification of the geometrical profile for a measured signature is critical. In addition, fast searching of the library is essential to find a best-matched signature even though the library may have huge amounts of data. The authors propose a support vector machine (SVM)-based method to deal with these issues. First, an SVM classifier is trained to identify the geometrical profile of a diffraction structure from its measured signature, and then another set of several SVM classifiers are trained to map the measured signature into a sublibrary to accelerate the search process. Simulations and experiments have demonstrated that the SVM classifier can identify the geometrical profile of one-dimensional trapezoidal gratings accurately, and the SVM-based library search strategy can achieve a fast and robust extraction of parameters for diffraction structures.
The library search is a widely used method for reconstruction of diffraction structures in optical scatterometry. In library
search, an optimized set of geometrical parameters for a diffraction structure can be achieved by searching for a best
match between the measured signatures and the simulated ones. The search speed and accuracy is the key to guarantee
the effectiveness of this method, and some a priori geometrical model is necessary. Once the actual geometrical model of
a measured signature is different from the model used in the establishment of library, the search result will be
meaningless. Therefore, the classification and recognition of the geometrical profile for a measured signature is critical.
In this paper, we develop two support vector machine (SVM) classifiers to deal with issue. One classifier is used to
identify the geometrical profile of a diffraction structure from its measured signature, and the other one is to map the
whole search range of the identified diffraction structure into a smaller one. By using some reliable and mature search
algorithms, we can fast and accurately reconstruct the geometry profile of a diffraction structure in this optimized small
range. Simulation and experiment have demonstrated that the SVM classifiers can identify the geometrical profile of
one-dimensional trapezoidal gratings accurately, and the SVM-based library search strategy can achieve a fast and
accurate extraction of parameters for diffraction structures.
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