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.
In-line Raman spectroscopy for compositional and strain metrology throughout front-end-of-line (FEOL) manufacturing of next-generation gate-all-around nanosheet field-effect transistors is presented. Thin and alternating layers of fully strained pseudomorphic Si(1 − x)Gex and Si were grown epitaxially on a Si substrate and subsequently patterned. Intentional strain variations were introduced by changing the Ge content (x = 0.25, 0.35, 0.50). Polarization-dependent in-line Raman spectroscopy was employed to characterize and quantify the strain evolution of Si and Si(1 − x)Gex nanosheets throughout FEOL processing by focusing on the analysis of Si-Si and Si-Ge optical phonon modes. To evaluate the accuracy of the Raman metrology results, strain reference data were acquired by non-destructive high-resolution x-ray diffraction and from destructive lattice deformation maps using precession electron diffraction. It was found that the germanium-alloy composition as well as Si and Si(1 − x)Gex strain obtained by Raman spectroscopy are in very good agreement with reference metrology and follow trends of previously published simulations.
In this work, the novel enhancement to multichannel scatterometry data collection, Spectral Interferometry, is introduced and discussed. The Spectral Interferometry technology adds unique spectroscopic data by providing absolute phase information. This enhances metrology performance by improving sensitivity to weak target parameters and reducing parameter correlations. Spectral Interferometry enhanced OCD capabilities were demonstrated for one of the most critical and challenging applications of gate-all-around nanosheet device manufacturing: lateral etching of SiGe nanosheet layers to form inner spacer indentations. The inner spacer protects the channel from the source/drain regions during channel release and defines the gate length of the device. Additionally, a methodology is presented, which enables reliable and reproducible manufacturing of reference samples with engineered sheet-specific indent variations at nominal etch processing. Such samples are ideal candidates for evaluating metrology solutions with minimal destructive reference metrology costs. Two strategies, single parameter and sheet-specific indent monitoring are discussed, and it was found that the addition of spectroscopic information acquired by Spectral Interferometry improved both optical metrology solutions. In addition to improving the match to references for single parameter indent monitoring, excellent sheet-specific indent results can be delivered
In-line Raman spectroscopy for compositional and strain metrology throughout front-end-of-line manufacturing of next generation stacked gate-all-around nanosheet field-effect transistors is presented. Thin and alternating layers of fully strained pseudomorphic Si(1-x)Gex and Si were grown epitaxially on a Si substrate and subsequently patterned. Intentional strain variations were introduced by changing the Ge content (x = 0.25, 0,35, 0.50). Polarization-dependent in-line Raman spectroscopy was employed to characterize and quantify the strain evolution of Si and Si(1-x)Gex nanosheets throughout front-end-of-line processing by focusing on the analysis of Si-Si and Si-Ge optical phonon modes. To evaluate the accuracy of the Raman metrology results, strain reference data were acquired by non-destructive high-resolution x-ray diffraction and from destructive lattice deformation maps using precession electron diffraction. It was found that the germanium-alloy composition as well as Si and Si(1-x)Gex strain obtained by Raman spectroscopy are in excellent agreement with reference metrology and follow trends of previously published simulations.
Over the past several years, stacked Nanosheet Gate-All-Around (GAA) transistors captured the focus of the semiconductor industry and has been identified as the new lead architecture to continue LOGIC CMOS scaling beyond-5nm node. The fabrication of GAA devices requires new specific integration modules. From very early processing points, these structures require complex metrology to fully characterize the three-dimensional parameter set. As the technology continues through research and development cycles and looks to transition to manufacturing, there are many opportunities and challenges remaining for inline metrology. Especially valuable are measurement techniques which are non-destructive, fast, and provide multi-dimensional feedback, where reducing dependencies on offline techniques has a direct impact to the frequency of cycles of learning. More than previous nodes, then, this node may be when some of these offline techniques jump from the lab to the fab, as certain critical measurements need to be monitored realtime. Thanks to the compute revolution this very industry enabled, machine learning has begun to permeate inline disposition, and hybrid metrology systems continue to advance. Metrology solutions and methodologies developed for prior technologies will also still have a large role in the characterization of these structures, as effects such as line edge roughness (LER), pitchwalk, and defectivity continue to be managed. This paper reviews related prior studies and advocates for future metrology development that ensures nanosheet technology has the inline data necessary for success.
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