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.
Semiconductor layer-to-layer overlay in manufacturing significantly impacts product quality and yield performance. Good control of device shifting also influences the spatial scale down for the nanoelectronics of memory applications. Advanced node DRAM semiconductor manufacturing requires a tighter in-die overlay budget. Typically, the inline overlay is measured by using a designed target in the scribe line. However, the difference between the metrology target and in-die device structure can lead to errors that can impact product quality and yield. This is especially true for complex structures such as the DRAM storage hole to landing pad overlay that cannot be well fabricated in the small target area. To meet the required tighter overlay control budget, the ability to measure in-die is essential. In this work, we introduce and demonstrate the line scan self-calibration solution for accurate and robust in-die overlay measurement of the storage node layer to the landing pad layer. Real spectra are collected by SpectraShape 11k dimensional metrology system where overlay splits are trained against the intended overlay and the SpectraShape 11k in-device overlay results are qualified by Set (designed overlay value from lithography), Get (overlay value measured by metrology tool) and TEM. Moreover, theoretical and experimental data show that the SpectraShape 11k Mueller elements are sensitive to tiny changes in the overlay parameters, which can enable robust, inline, high throughput overlay metrology. We demonstrate that the SpectraShape 11k successfully measures the in-die overlay of the complex storage hole to the landing pad structure with good accuracy and high throughput thereby contributing to improved process control and yield improvement.
Today, with the accelerating complexity of nanoelectronics for memory applications, in-die overlay metrology has required much tighter control. A typical in-device overlay control strategy utilizes high-voltage SEM metrology across several key layers, but lot and wafer sampling is limited due to low system throughput. Our objective is to find a faster, more robust, and more efficient optical metrology solution that can produce the same in-die overlay results vs. SEM. In this work, we create a novel solution using the KLA SpectraShape 11k dimensional metrology system to demonstrate improved nonzero overlay (NZO) control that meets the tighter overlay budget requirement. We combined the spectroscopic Mueller matrix of SpectraShape 11k and the machine learning algorithm of TurboShape modeling software. Both real spectra collected by SpectraShape 11k and theoretical spectra generated from the scatterometry model are trained against their corresponding SEM reference and synthetic reference data respectively to predict the overlay value. Accurate and robust optical in-device overlay results are proven with a high correlation to the HV-SEM data. In addition, the SpectraShape 11k in-device overlay is equipped with a few key performance indicators (KPIs) including CIndex and CD profile, which are designed to flag process excursions in an HVM environment. Good agreement is observed between the KPIs and overlay delta to HV-SEM. Finally, the 4x-8x throughput advantage of optical metrology in-device overlay vs. SEM in-device overlay allows users to set more dense wafer measurements by lot or dense site measurements by wafer, enabling better lot-to-lot or wafer-to-wafer NZO control.
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.
Advanced technology nodes require tighter lithography overlay specifications with higher throughput and lower cost of ownership. Today, with the accelerating complexity of nanoelectronics for memory applications, an increased emphasis is placed on controlling the on-product overlay (OPO) budget. Consequently, accurate in-die overlay measurements play a critical role after the etching process (ACI) for which it can better reflect the actual product overlay. Here we propose a solution with the combined spectroscopic full Mueller matrix, measured with the KLA next-generation SpectraShape™ dimensional metrology system and a physics-based machine learning algorithm. Both real spectra collected by the SpectraShape and theoretical spectra generated from the scatterometry model are trained against their corresponding ground truth reference and synthetic reference data respectively to predict overlay. Theoretical and experimental results show that the Mueller elements are sensitive to very small changes in the overlay parameters which can enable inline, high-throughput overlay metrology. Accuracy, robustness, and precision on massive datasets using design of experiments (DOE) wafers are presented and discussed. Moreover, the measurement reliability is assessed with a key performance indicator (KPI), designed to flag a process excursion in a high-volume manufacturing (HVM) environment. Good agreement is observed between the KPI and the actual model accuracy.
Defects in ultra-thin films appear as small perturbations in the measured optical dispersion using spectroscopic ellipsometry (SE). A common approach for quantifying these defects is to fit each pixel in the dispersion to an index of refraction and extinction coefficient for a known material thickness (point-by-point method). However, this point-bypoint method is not physical because it produces dispersions that are not Kramers-Kronig consistent and it is also subject to overfitting. In this work, we demonstrate that the Kramers-Kronig consistent Cody Lorentz Multiple-Oscillator model (CLM) can precisely quantify defects in HfO2 using the Lorentz peak amplitude dispersion parameter as one of the fitting parameters. Using a KLA-Tencor spectroscopic ellipsometer, we collected optical dispersions of ultra-thin HfO2 grown on SiO2 for a variety of growth parameters including HfO2 thickness, SiO2 thickness, and anneal time, and then have used CLM to quantify the defects. The HfO2 defect value was found to successfully track the different growth conditions, which is consistent with literature, and the defect values have little within-wafer variance. Quantifying defects in a material sub-bandgap successfully will provide information about leakage currents and device performance for gated semiconductor devices.
At the 28nm node using 300mm wafers, oxide step height in STI CMP transient gate after-etch inspection (TG AEI)
wafers is a critical parameter that affects device performance and should be monitored and controlled. For production
process control of this kind of structure, a metrology tool must utilize a non-destructive measurement technique, and
have high sensitivity, precision and throughput [1]. This paper discusses a scatterometry-based measurement method for
monitoring critical dimension step height in STI CMP instead of traditional measurement methods such as atomic force
microscopy (AFM). The scatterometry tool we used for our investigations was the KLA-Tencor SpectraShape 8810,
which is the most recent model of the spectroscopic critical dimension (SCD) metrology tools that have been
implemented in production for process control of TG AEI structures. AFM was used as a reference metrology technique
to assess the accuracy performance of the SpectraShape8810. The first objective of this paper is to discuss the best
azimuth angle and floating parameters for scatterometry measurement of the step height feature in TG AEI wafers.
Second, this paper describes the tool matching performance of SpectraShape 8810 and correlation to AFM determined
using a DOE of TG AEI wafers.
Scatterometry-based metrology measurements for advanced gate after-develop inspection (ADI) and after-etch
inspection (AEI) structures have been well proven1. This paper discusses the metrology challenges encountered in
implementing a production-worthy methodology for accurately measuring gate ADI middle CD (MCD) and sidewall
angle (SWA) to monitor focus and exposure dose. A Multi-Target Measurement (MTM) methodology on KLA-Tencor's
SpectraShape 8810 was evaluated on its ability to characterize and measure FEM (Focus Exposure Matrix) and EM
(Exposure Matrix) wafers. The correlation of MCD and SWA to the focus and exposure dose was explored. CD-SEM
measurements were used as a reference to compare the accuracy of scatterometry MCD measurements. While there was
no reference tool available to compare scatterometry SWA measurements, the SWA and focus tracking on the FEM
wafer were verified. In addition to the MTM methodology evaluation, a fleet of four SpectraShape 8810 tools was
evaluated to measure the fleet's capability for in-line monitoring in high volume manufacturing. The final results
confirmed that the Multi-Target Measurement approach on SpectraShape 8810 is an effective solution for gate ADI
metrology and the robust fleet matching performance would enable in-line monitoring use.
Advanced integrated circuit (IC) manufacturing requires high quality metrology for process disposition and control in
order to achieve high yields. As the industry advances in high volume manufacturing of 3x and 2x nm nodes with the
associated advanced materials and complex structures, understanding and reducing film and critical dimension (CD)
measurement uncertainty is more critical than ever. Optical film metrology is used for measurement of critical film
parameters such as n & k, thickness and composition, while optical CD metrology is used for measurement of CD,
sidewall angle (SWA), height, and other structure-related parameters. Both optical film and CD metrologies utilize
advanced structure modeling that includes fitting parameters of the device stack for multiple layers simultaneously.
These methods have been proven and established in both R&D and high volume manufacturing scenarios. As film stacks
and structures become more complex and design tolerances shrink, however, additional parameters need to be included
in the modeling, in some cases leading to reduced parameter precision and unwanted parameter correlation. In this paper
we discuss a new methodology, Data Feed Forward, that utilizes multiple metrology steps, and the feed forward of the
derived parameters to next metrology steps, for improved measurement sensitivity and quality. In addition, we discuss
Data Feed Forward requirements for fab-wide implementation.
KEYWORDS: Silicon, Single crystal X-ray diffraction, Etching, 3D metrology, 3D modeling, Process control, Scatterometry, Metrology, Data modeling, Semiconducting wafers
As DRAM design advances from planar to vertical integration, process control of the recessed gate, generated by etching
after patterning in vertical DRAM, is very critical because of the impact on device electrical characteristics and
subsequent effect on yield. 3D Scatterometry Critical Dimension (3D SCD) technology is a widely-used metrology
approach for process control for leading edge CMOS and DRAM IC manufacturing.
In this paper, the latest KLA-Tencor AcuShapeTM modeling software with 3D SCD capability is used in the
modeling and solution development, and the SpectraShapeTM 8660 is used for data collection and CD measurement.
Recess gate measurements were taken in the active cell area having a non-orthogonal structure. The SCD measurement
results were successfully confirmed to correlate well with cross-section Scanning Electron Microscope (X-SEM) and
electrical performance data.
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