3D flash memory structures have been rapidly developed over the past decade to achieve a high density of stacked memory cells with periodic channel holes across the device. Small deviations of the hole shape can result in considerable variations in device performance and product yield. Understanding the behavior and performance of these high-aspect-ratio structures plays a vital role in such complex vertically stacked structures. Memory hole critical dimensions (CDs) serve as the key information to evaluate the performance of 3D flash memory devices, and these CDs are typically measured by rigorous coupled wave analysis (RCWA) based optical metrology that requires costly and destructive scanning electron microscopy (SEM) or transmission electron microscopy (TEM) reference. Here, we utilize critical dimension smallangle x-ray scatterometry (CD-SAXS) that provides reliable and nondestructive ground truth reference to extract a large amount of detailed hole shape information within a practical time scale compared to traditional lengthy TEM measurements. We leverage advanced data analytics and machine learning techniques to enable an optical critical dimension metrology solution along with the desired amount of reference from CD-SAXS measurements for the memory hole profile investigation. This proposed methodology opens up a new venue for a high-throughput, robust and accurate hole CD profile measurement for the fast-paced and high-volume 3D flash memory manufacturing technology.
The semiconductor industry has witnessed a fast progression of spectroscopic ellipsometry (SE) techniques aimed at resolving a plethora of complex device characterizations on a nanometric scale. The Mueller Matrix (MM) methodology coupled with rigorous coupled-wave analysis (RCWA) has offered an unprecedented power of investigation and analysis of diverse critical dimensions (CDs), especially when applied to gate-all-around (GAA) structures, as it helps increase the useful spectral signals of the often geometrically buried CDs. However, the sensitivity to the CDs can be often screened by other parameters, hampering the precision and accuracy of the measurement. Combining the most sensitive MM elements has therefore become a critical step of scatterometry critical dimension (SCD) metrology. Driven by the rapid developments of Machine Learning (ML) algorithms, we propose a versatile ellipsometry methodology that overcomes poor sensitivity and increases accuracy through a novel principal component analysis (PCA) method of the ML training algorithm with RCWA assistance. Furthermore, our methodology introduces a new ML training concept based on reference data statistics, rather than raw reference. Our approach has been validated with reference data and proved successful in monitoring GAA sheet-specific indent. The proposed methodology paves the way to measuring low sensitivity CDs with highly accurate, noise-reduced and robust ML-based physical SCD models for any logic and memory application.
The complex vertically stacked gate-all-around (GAA) manufacturing process drives the demand for more challenging inline metrology requirements. GAA technology with specific technical requirements starts from the first process step, 1) the superlattice, where the multi-stack Si/SiGe pairs must be grown defect-free with matched Si nanosheet thicknesses, and %Ge per layer, sharp interfaces, and a minimized subsequent thermal Ge diffusion across the stacks. More critical steps, among others, are the 2) partial recess of the sacrificial SiGe layers that precede 3) the inner spacers which prevent a channel to source/drain short circuit and reduce the parasitic capacitance, and 4) the channel release process at the “remove poly gate” module, where the SiGe is selectively removed before the high-k metal gate. Driven by tight performance control, a sheet-specific metrology solution is highly desired at each of the above four critical steps. The ideal solution for such an application is non-destructive, precise, accurate, and highly productive. In this paper, a scatterometry critical dimension (SCD) solution for the GAA sheet-specific measurement from various GAA structures is presented. The SCD solution includes an advanced and optimized full Mueller Matrix spectroscopic ellipsometry in conjunction with a physics-assisted machine learning (ML) algorithm. Additionally, the best methodology to address the solution's robustness to process variation is described and presented. It will be shown that an optimized signal-to-noise ratio combined with ML can provide a superior optical metrology solution to the growing challenge in GAA applications.
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