This paper introduces a new AI-enpowered method for accurately measuring submicron structures with high aspect ratios (HAR) in semiconductor packaging using spectral scatterometry across DUV, visible, and SWIR wavelengths. By optimizing polarization and spectrometer calibration, the method improves spectral signal contrast for precise critical dimension (CD) metrology. An Artificial Neural Network (ANN) tackles phase shift problems for trench spacings near light wavelengths, enabling precise CD measurement. Experiments demonstrate DUV light's proficiency in measuring small CD differences and VIS and SWIR's effectiveness for larger, deeper structures. The DUV system measures HARs up to 10:1 and apertures down to 0.46 μm with accuracy within 3% of Focused Ion Beam/Scanning Electron Microscope (FIB/SEM) comparisons.
This research means to solve the challenges in measuring deep high-aspect-ratio (HAR) and thin-film structures in 3D integrated circuits. As the semiconductor industry reaches its physical limitations in device scaling, advanced technologies such as advanced lithography and packaging have become crucial in extending Moore's law. This has led to the use of denser nano-to-sub-micron structures in three-dimensional integrated circuits (3D-IC), resulting in smaller, more functional devices. However, measuring these complex and deep HAR and thin-film structures with a large depth range from a few nanometers to a few hundred micrometers using a single optical system is challenging. To address this need, this article presents an AI-guided scatterometry method using numerical aperture control to achieve a large measurement range. The system uses broadband light to generate multi-wavelength reflection responses from the samples. With the help of an electromagnetic simulation tool and an artificial neural network model, the depth resolution can be improved through inverse modeling. The results demonstrate the ability to measure a wide range of samples with depths ranging from nanometers to micrometers scale, including sub-micron HAR openings and ultra-thin films, as long as the measurement bias is controlled within acceptable limits.
A new non-integral optical scatterometry technique has been introduced to circumvent issues with traditional methods in the critical dimension (CD) characterization of micro and nano-structures in semiconductor inspections. This method uses the high spatial coherence of the laser source, and an adjustable numerical aperture (NA) for effective beam shaping, enabling precise measurement of high-aspect-ratio structures. It incorporates a model-based approach with a virtual optical system and the Finite- Difference Time-Domain (FDTD) method for multiple CD characterizations, improving measurement precision. Early tests indicate a minimal average bias of 1.74% from calibrated references and standard deviations within 7 nm.
A neural network-assisted spectral scatterometry method is presented to measure multi-dimensional critical dimensions (CDs) on high aspect ratio (HAR) structures with micron or submicron scales. With the rise of 3D integrated circuit packaging, there is a need for accurate characterization of HAR sub-micron structures. This method uses DUV scatterometry and a broadband light source from DUV to visible light to gather multi-channel reflection data. The inverse modeling method and artificial neural network model enable accurate measurement of multiple CDs of test structures. The results showed accurate measurement of deep trench critical dimensions with a nominal line width of 0.6 μm and aspect ratio up to 5:1, with accuracy within a few nanometers comparable to SEM results using the same sample.
Global semiconductor packaging manufacturers are developing advanced process technologies with the rapid rise of heterogeneous packaging and 3D packaging. High-aspect-ratio (HAR) structures like through silicon vias (TSV) or redistribution layers (RDL) that come with the prevalence of 3D packaging technology have further significantly increased the difficulties in optical critical dimension (OCD) metrology. Due to emerging technical challenges, effective sub-micron HAR OCD solutions are highly demanded to resolve the technical bottleneck. Thus, This article presents an AI-guided method for simulating and building a training dataset using the finite difference time domain (FDTD), then forming a DNN model for reconstructing CDs. At the same time, an optical scatterometry-based microscope was developed to adopt optical light capable of penetrating a sub-micron opening size structure and characterizing critical dimensions such as top critical dimension (TCD) and depth. In the optical system design, the optical FOV can be narrowed down only to cover a single sub-micron structure for OCD metrology. A preliminary test verified that a single sub-micron structure with an aspect ratio of 1:3.3 and the maximum bias between the measured data and the SEM references could be kept within a few tens of nanometers for its depth, TCD, and BCD measurement.
The article presents a novel optical metrology method for accurate critical dimension (CD) measurement of sub-micrometer structures with high spatial resolution and light efficiency. The proposed method takes advantage of the spatially coherent nature of the supercontinuum laser to detect submicron-scale structures with high aspect ratios. By using the method, CD measurement of individual microstructures such as vias and redistribution layers (RDL) becomes achievable when a high magnification optical configuration is incorporated. Proved by a test run on measuring submicron structures with linewidths as small as 0.7 μm and an aspect ratio over 4, the measurement precision of the depth can be kept within a few nanometers.
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