As device scaling continues, controlling defect densities on the wafer becomes essential for high volume manufacturing (HVM). One type of defect, the non-selective SiGe nodule, becomes more difficult to control during SiGe epitaxy (EPI) growth for p-type field effect transistor (pFET) source and drain. The process window for SiGe EPI growth with low nodule density becomes extremely tight due to the shrinking of contact poly pitch (CPP). Any tiny process shift or incoming structure shift could introduce a high density of nodules, which could affect device performance and yield. The current defect inspection method has a low throughput, so a fast and quantitative characterization technique is preferred for measuring and monitoring this type of defect.
Scatterometry is a fast and non-destructive in-line metrology technique. In this work, novel methods were developed to accurately and comprehensively measure the SiGe nodules with scatterometry information. Top-down critical dimension scanning electron microscopy (CD-SEM) images were collected and analyzed on the same location as scatterometry measurement for calibration. Machine learning (ML) algorithms are used to analyze the correlation between the raw spectra and defect density and area fraction. The analysis showed that the defect density and area fractions can be measured separately by correlating intensity variations. In addition to the defect density and area fraction, we also investigate a novel method – model-based scatterometry hybridized with machine learning capabilities – to quantify the average height of the defects along the sidewall of the gate. Hybridizing the machine learning method with the model-based one could also eliminate the possibility of misinterpreting the defect as some structural parameters. Furthermore, cross-sectional TEM and SEM measurement are used to calibrate the model-based scatterometry results. In this work, the correlation between the SiGe nodule defects and the structural parameters of the device is also studied. The preliminary result shows that there is strong correlation between the defect density and spacer thickness. Correlations between the defect density and the structural parameters provides useful information for process engineers to optimize the EPI growth process. With the advances in the scatterometry-based defect measurement metrology, we demonstrate such fast, quantitative, and comprehensive measurement of SiGe nodule defects can be used to improve the throughput and yield.
Extreme Ultraviolet lithography requires defect free multilayer-coated masks. The defects in multilayer-coated masks originate from several sources including: the incoming substrate, pre-multilayer deposition cleaning, multilayer deposition, and handling processes. A previous study showed the majority of currently detectable defects are contributed by the incoming substrate. The purpose of this study is to understand the ability of multilayer deposition to modulate the size and shape of substrate pits, and to, ultimately, enable us to determine if a defect of a particular size and shape is tolerable, and will result in a non-printable pit after coating. In order to execute a systematic study, pits with controlled sizes and shapes were required. Programmed pit arrays were generated using Focused Ion Beam (FIB). The arrays were designed to contain pits of various widths and depths. The physical size of these pits was measured using Atomic Force Microscope (AFM) and Scanning Electron Microscope (SEM) both before and after multilayer deposition. These programmed pit arrays were also used to probe the sensitivity of a state of the art Lasertec M1350 defect inspection system to defect size and shape both before and after coating. Finally, the results were compared to those from natural pits. The programmed defects generated in this study will also enable further development of defect mitigation by other planarization techniques as well as improving inspection recipes.
One of the main challenges for EUV mask blank metrology is that most tools are designed for either; 1) wafer handling, 2) off-line characterization, or 3) destructive failure analysis. Few clean room-compatible metrology tools for full EUV mask blanks are commercially available. At International SEMATECH's EUV Mask Blank Development Center (EUV MBDC), in Albany NY, we have partnered closely with both metrology and tool integration vendors to modify tools in order to meet stringent EUV requirements. We have succeeded in integrating SMIF-based mask handling metrology tools in a clean room environment. We have demonstrated seamless mask blank defect identification and characterization by coordinate mapping and transfer from our defect inspection tool to both AFM and FIB-SEM/EDX. Additionally, we have successfully integrated these tools with a software package specifically designed for mask yield improvement- the first deployment of its kind targeted specifically for EUV mask defect reduction. The net result is a state-of-the-art EUV metrology toolset capable of identifying, characterizing, and correlating defects on both EUV mask blanks and bare substrates. The facility is currently capable of analyzing the defects as small as 50 nm, with 30 nm capability forecasted in 2006.