Pietro Montanini

A 7nm FinFET technology featuring EUV patterning and dual strained high mobility channels

We present a 7nm technology with the tightest contacted poly pitch (CPP) of 44/48nm and metallization pitch of 36nm ever reported in FinFET technology. To overcome optical lithography limits, Extreme Ultraviolet Lithography (EUV) has been introduced for multiple critical levels for the first time. Dual strained channels have been also implemented to enhance mobility for high performance applications.

Stacked nanosheet gate-all-around transistor to enable scaling beyond FinFET

In this paper, for the first time we demonstrate that horizontally stacked gate-all-around (GAA) Nanosheet structure is a good candidate for the replacement of FinFET at the 5nm technology node and beyond. It offers increased Weff per active footprint and better performance compared to FinFET, and with a less complex patterning strategy, leveraging EUV lithography. Good electrostatics are reported at Lg=12nm and aggressive 44/48nm CPP (Contacted Poly Pitch) ground rules. We demonstrate work function metal (WFM) replacement and multiple threshold voltages, compatible with aggressive sheet to sheet spacing for wide stacked sheets. Stiction of sheets in long-channel devices is eliminated. Dielectric isolation is shown on standard bulk substrate for sub-sheet leakage control. Wrap-around contact (WAC) is evaluated for extrinsic resistance reduction.

FINFET technology featuring high mobility SiGe channel for 10nm and beyond

SiGe for channel material has been explored as a major technology element after the introduction of FINFET into CMOS technology [1-4]. Research on long channel FETs and discrete short channel FETs demonstrated benefits in mobility [1-4] and reliability [2]. Given the disruption that SiGe FIN brings, every aspect associated with SiGe FIN needs to be carefully studied towards technology insertion. In this paper, we report the latest SiGe-based FINFET CMOS technology development. CMOS FINFETs with Si-FIN nFET and SiGe-FIN pFET is demonstrated as a viable technology solution for both server and mobile applications at 10nm node and beyond.

Technology viable DC performance elements for Si/SiGe channel CMOS FinFTT

Low Ge content SiGe-based CMOS FinFET is one of the promising technologies [1-2] offering solutions for both high performance and low power applications. In this paper, we established a competitive SiGe-based CMOS FinFET baseline and examined various elements for high performance offering. The performance elements in gate stack, channel doping, contact resistance, and junction have been explored to provide a cumulative 20% / 25% (n/pFET) performance enhancement. These elements provide a viable path towards performance enhancement for future technology nodes.

In-line characterization of non-selective SiGe nodule defects with scatterometry enabled by machine learning

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.