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.
Multi-channel gate all around (GAA) semiconductor devices require measurements of more target parameters than FinFET devices, due in part to the increased complexity of the different structures needed to fabricate nanosheet devices. In some cases, multiple measurement techniques are required to be used in a hybrid-metrology technique in order to properly extract the necessary information. Optical scatterometry (optical critical dimension, or OCD) is an inline metrology technique which is used to measure the geometrical profile of the structure, but it may not ordinarily be sensitive to very small residues. X-ray based metrologies, such as x-ray fluorescence (XRF) can be used to identify which materials are present in the structure, but are not able to measure profile information for complex 3D structures.
This paper reviews a critical etch process step, where neither OCD nor XRF can extract all of the necessary information about the structure on their own, but, when hybridized, are able to provide enough information to solve the application. In GAA structures, the nanosheets are formed from alternating layers of thin SiGe and Si layers which are deposited on a bulk Si substrate. To form the nFET channel, the SiGe must be removed. However, in some cases, there is still remaining SiGe residue on the surface of the Si nanosheets, present in small amounts that are difficult to measure with conventional OCD. Additionally, it is desirable to know at which level of the stacked nanosheets the residue is present. In order to properly characterize the amount of SiGe remaining, data from both OCD and XRF are used. By measuring before and after the etch, the XRF can calculate the percentage of SiGe that is remaining after the etch. This percentage can be used as a constraint in the OCD model to allow the OCD to accurately measure the amount of SiGe, and to enable the OCD model to identify the location of the residue.