Total measurement uncertainty (TMU) is a commonly used key performance indicator (KPI) for tool-induced error in metrology systems. Several definitions of TMU are being used today for overlay metrology (OVL), with the leading definition being the root-sum-square (RSS) of three other KPIs: the wafer mean Tool Induces Shift (TIS𝜇), the wafer variability of TIS (TIS3σ), and the OVL measurement reproducibility (OVL precision). A multitude of TIS management methods has been developed and implemented over the years for calibrating out the raw TIS from OVL. With these TIS management methods in place, the use of the raw TISμ and TIS3σ in TMU no longer serves as a good characterization of the total tool-induced error. In this paper, we describe a procedure for evaluating the actual, post-TIS management, OVL Metrology TMU through the introduction of two new wafer level indicators: the effective wafer means TIS (eTISμ), and the effective wafer TIS variability (eTIS3σ).
Overlay metrology plays a significant role in process and yield control for integrated circuit (IC) manufacturing. As the On-Product Overlay (OPO) in advance nodes is reduced to a few nanometers, a very small margin is left for measurement inaccuracy. We introduce a multi-wavelength (spectral) analysis and measurement method, capable of characterizing overlay inaccuracy signatures on the wafer, and quantifying and removing the inaccuracy portion of the overlay measurement, resulting in a more accurate measurement, better process control, and yield enhancement. This method was applied to SK hynix’s advanced process production wafers, demonstrating an enhancement in accuracy over single-wavelength based overlay measurements.
In recent technology node manufacturing processes, on-product overlay (OPO) is becoming increasingly more important. In previous generations, the optimization of the total measurement uncertainty (TMU) itself was sufficient. However, with the use of modern technologies, target asymmetry-related measurement inaccuracy became a significant source of error, requiring new methods of control. This paper presents a machine learning (ML) based algorithm that reduces inaccuracy in misregistration measurements of the after-develop inspection (ADI) optical overlay (OVL). The algorithm relies on numerous features that were extracted from the OVL tool camera images, accuracy metrics derived from OVL computation, and other metadata. It is trained to estimate OVL measurement inaccuracy and produce corrected OVL per site. The ground truth of the ML model can include either internal or external OVL values. In the former case, the model is trained using wafer modeling errors (a.k.a. residuals), implying that these are a good indicator of target inaccuracy, which is a commonly used assumption. In the latter case, the model is trained using external overlay as the reference. If an accurate external reference overlay measurement exists, this option can be the most accurate. In both cases, the algorithm produces corrected OVL values. This study shows that for both ground truth options, the suggested method reduces inaccuracy and wafer modeling residuals in ADI optical OVL metrology measurements. The results were obtained by experimenting on production wafers from DRAM critical layers at SK Hynix. All the measurements were taken using an imaging-based overlay (IBO) technique and were validated by scanning electron microscope (SEM) measurements of the same wafers.
Overlay process control is a critical aspect of integrated circuit manufacturing. Advanced DRAM manufacturing overlay error budget approaches the sub-2nm threshold, including all sources of overlay error: litho processing, non-litho processing, metrology error, etc. Overlay measurement quality, both for accuracy and robustness, depends on the metrology system and its recipe setup. The optimal configuration depends on the layer and materials involved. Increased flexibility of metrology setup is of paramount importance, paired with improved methods of recipe optimization.
Both optical image-based overlay (IBO) and scatterometry diffraction overlay (SCOL®) are necessary tools for overlay control. For some devices and layers IBO provides the best accuracy and robustness, while on others SCOL provides optimum metrology. Historically, wavelength selection was limited to discrete wavelengths and at only a single wavelength. At advanced nodes IBO and SCOL require wavelength tunability and multiple wavelengths to optimize accuracy and robustness, as well as options for polarization and numerical aperture (NA). In previous studies1,2,3 we investigated wavelength tunability analysis with landscape analysis, using analytic techniques to determine the optimal setup. In this report we show advancements in the landscape analysis technique for IBO through both focus and wavelength, and comparisons to SCOL. A key advantage of imaging is the ability to optimize wavelength on a per-layer basis. This can be a benefit for EUV layers in combination with those of 193i, for example, as well as other applications such as thick 3D NAND layers. The goal is to make accurate and robust overlay metrology that is immune from process stack variations, and to provide metrics that indicate the quality of metrology performance. Through both simulation and on-wafer advanced DRAM measurements, we show quantitative benefits of accuracy and robustness to process stack variability for IBO and SCOL applications.
Methodologies described in this work can be achieved using Archer™ overlay metrology systems, ATL™ overlay metrology systems, and 5D Analyzer® advanced data analysis and patterning control solution.
In overlay (OVL) metrology the quality of measurements and the resulting reported values depend heavily on the measurement setup used. For example, in scatterometry OVL (SCOL) metrology a specific target may be measured with multiple illumination setups, including several apodization options, two possible laser polarizations, and multiple possible laser wavelengths. Not all possible setups are suitable for the metrology method as different setups can yield significantly different performance in terms of the accuracy and robustness of the reported OVL values. Finding an optimal measurement setup requires great flexibility in measurement, to allow for high-resolution landscape mapping (mapping the dependence of OVL, other metrics, and details of pupil images on measurement setup). This can then be followed by a method for analyzing the landscape and selecting an accurate and robust measurement setup. The selection of an optimal measurement setup is complicated by the sensitivity of metrology to variations in the fabrication process (process variations) such as variations in layer thickness or in the properties of target symmetry. The metrology landscape changes with process variations and maintaining optimal performance might require continuous adjustments of the measurement setup. Here we present a method for the selection and adjustment of an optimal measurement setup. First, the landscape is measured and analyzed to calculate theory-based accurate OVL values as well as quality metrics which depend on details of the pupil image. These OVL values and metrics are then used as an internal ruler (“self-reference”), effectively eliminating the need for an external reference such as CD-SEM. Finally, an optimal measurement setup is selected by choosing a setup which yields the same OVL values as the self-reference and is also robust to small changes in the landscape. We present measurements which show how a SCOL landscape changes within wafer, wafer to wafer, and lot to lot with intentionally designed process variations between. In this case the process variations cause large shifts in the SCOL landscape and it is not possible to find a common optimal measurement setup for all wafers. To deal with such process variations we adjust the measurement setup as needed. Initially an optimal setup is chosen based on the first wafer. For subsequent wafers the process stability is continuously monitored. Once large process variations are detected the landscape information is used for selecting a new measurement setup, thereby maintaining optimal accuracy and robustness. Methods described in this work are enabled by the ATL (Accurate Tunable Laser) scatterometry-based overlay metrology system.
Overlay is one of the most critical process control steps of semiconductor manufacturing technology. A typical advanced scheme includes an overlay feedback loop based on after litho optical imaging overlay metrology on scribeline targets. The after litho control loop typically involves high frequency sampling: every lot or nearly every lot. An after etch overlay metrology step is often included, at a lower sampling frequency, in order to characterize and compensate for bias. The after etch metrology step often involves CD-SEM metrology, in this case in-cell and ondevice. This work explores an alternative approach using spectroscopic ellipsometry (SE) metrology and a machine learning analysis technique. Advanced 1x nm DRAM wafers were prepared, including both nominal (POR) wafers with mean overlay offsets, as well as DOE wafers with intentional across wafer overlay modulation. After litho metrology was measured using optical imaging metrology, as well as after etch metrology using both SE and CD-SEM for comparison. We investigate 2 types of machine learning techniques with SE data: model-less and model-based, showing excellent performance for after etch in-cell on-device overlay metrology.
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