We configured a spectroscopic ellipsometer (SE) optical system with a micrometer spot size using reflective objectives. Two reflective objectives, based on the Schwarzchild configuration, are positioned just before and after the beam reflects on the sample. While we achieved the appropriate angle of incidence and numerical aperture (NA) of the light beam on the sample by utilizing an off-axis reflection configuration for the objectives, the polarization state of light is significantly influenced due to its asymmetric beam path, beam focusing, and reflection on the mirror surfaces. Therefore, we developed a proper model to describe the polarization state change arising from the effect of reflective objectives for our custom Spectroscopic Ellipsometer optical system and demonstrated its applicability for thin film structures.
KEYWORDS: 3D modeling, 3D applications, Semiconductors, Deep learning, Metrology, Algorithm development, Mathematical optimization, Evolutionary algorithms, 3D metrology, Software development
This paper introduces our development of software designed for OCD (Optical Critical Dimension) modeling, utilizing 3D graphics design functionality. In the OCD metrology, the role of analysis software is crucial for accurately and precisely extracting CD parameters from intricate device structures. Our software incorporates calculation engines grounded in Physics and Machine Learning - RCWA (Rigorous Coupled Wave Analysis) and DL (Deep Learning). The software's advanced 3D modeling engine supports complex structure manipulation and precise adjustments of a broad range of parameters, including optical properties. This facilitates detailed device geometry exploration through a cohesive interface. The DL algorithm has been developed ensuring consistency between RCWA and DL predictions, essential for accurate and rapid OCD metrology. We have conducted a comprehensive evaluation process to assess the consistency between RCWA calculations and 3D representations, encompassing both 2D and 3D structures. Further evaluation is planned, specifically focusing on real patterned wafers.
We investigate on how to improve the performance of thickness determination from the optical scatterometry spectrum using machine learning. Our investigation is focused on a specific application for thick layered structures for 3D NAND with oxide/nitride repeating pairs. Since fast determination of thickness of every layer or detection of any outlier is not very efficient with the regression analysis using an optical model-based calculation due to requirement of a huge amount of calculations along with many parameters, machine learning (ML) can be applied for this application pursuing a faster solution. However, we also need to achieve its precision and accuracy as good as possible under a limited amount of ML train data sets. In order to carry out an efficient extraction or selection of features from data which is very important for improved performance of ML, we applied Fourier analysis of spectrum and investigate on how ML performance is improved.
We report on GaN-based vertical cavities on highly reflective and crack-free 40.5 pair of AlGaN/GaN distributed Bragg
reflectors (DBRs) by using a selective growth method to avoid wafer cracking that is commonly observed in
conventional planar Al(Ga)N/GaN DBRs. An Al0.46Ga0.54N/GaN DBR with ~ 98% reflectivity was selectively grown
with square patterns of up to 150 × 150 μm2 in size, which were separated from each other by 10 μm wide SiO2 mask
stripes. Vertical cavity structures employing InGaN/InGaN multiple quantum wells (MQWs) were grown on these crackfree
patterned DBRs and capped with 13 pair SiO2/SiNx DBRs to complete the full cavity structure. A cavity mode at ~
442 nm in 150 × 150 μm2 area was observed, having a quality factor of ~300. The selective growth technique to
eliminate crack formation is very promising for the fabrication of nitride-based vertical-cavity surface emitting laser
devices.
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