Dual beam focused ion beam/scanning electron microscopy (FIB/SEM) is a critical characterization technique that is used as inline metrology from early stages of process developments until high volume manufacturing (HVM) of magnetic read/write heads in hard disk drive (HDD) due to the complex three-dimensional geometry [1]. Despite its destructive nature, FIB/SEM metrology is critical to support high throughout manufacture process for advanced process control during HVM in HDD industry. Final cross-sectional SEM images typically include several CD measurements and embedded or standalone standard machine vision applications are used as part of the metrology process. However, these applications are typically not able to accommodate various process changes during the rapid process development, and manual engineer assistance are often needed for the accurate cut placement and SEM search. On the other hand, optimization of machine vision application typically requires a reasonable number of images to allow training and optimization of edge finder and pattern recognition functions. Reducing the training and optimization time needed for machine vision applications reduces the learning time during new process development. In this work, we are introducing a machine learning based metrology application that minimizes the need for engineer involvement for recipe optimization during the rapid process development [2]. By addition of the process margin entities to the machine learning model, the recipe robustness is significantly improved at the time of transition to new product introduction (NPI) and high volume manufacturing (HVM). We compare the new machine learning based metrology application against the legacy machine vision application and study its impact on recipe writing time, wafer to wafer variations, and total measurement uncertainty (TMU). The new application allows recipes capable of cross-design metrology.
KEYWORDS: Metrology, Ion beams, Process control, Semiconducting wafers, Scanning electron microscopy, Head, Ions, Magnetism, Error analysis, Electron beams, 3D metrology, Wafer manufacturing, Hardware product development
Dual beam focused ion beam/scanning electron microscopy (FIB/SEM) is a key characterization technique for rapid process development of electronic devices with complex geometry such as magnetic read/write heads in hard disk drives (HDD). Despite the destructive nature of FIB/SEM, it is still used as an in-line metrology technique supporting high volume manufacturing (HVM) process control. To overcome the throughput limitation of this technique and minimize the impact on product shipment time, it is a common practice to have a fleet of FIB/SEM tools in line. Hence, controlling the total measurement uncertainty (TMU) for the reference metrology fleet is essential. However, the existing TMU evaluation methods are mainly developed for non-destructive or less-destructive metrology techniques, which allows measurement repetition. [1], [2]
As the feature size is shrinking in the foundries, the need for inline high resolution surface profiling with versatile capabilities is increasing. One of the important areas of this need is chemical mechanical planarization (CMP) process. We introduce a new generation of atomic force profiler (AFP) using decoupled scanners design. The system is capable of providing small-scale profiling using XY scanner and large-scale profiling using sliding stage. Decoupled scanners design enables enhanced vision which helps minimizing the positioning error for locations of interest in case of highly polished dies. Non-Contact mode imaging is another feature of interest in this system which is used for surface roughness measurement, automatic defect review, and deep trench measurement. Examples of the measurements performed using the atomic force profiler are demonstrated.
Single crystal silicon wafers are the fundamental elements of semiconductor manufacturing industry. The wafers produced by Czochralski (CZ) process are very high quality single crystalline materials with known defects that are formed during the crystal growth or modified by further processing. While defects can be unfavorable for yield for some manufactured electrical devices, a group of defects like oxide precipitates can have both positive and negative impacts on the final device. The spatial distribution of these defects may be found by scattering techniques. However, due to limitations of scattering (i.e. light wavelength), many crystal defects are either poorly classified or not detected. Therefore a high throughput and accurate characterization of their shape and dimension is essential for reviewing the defects and proper classification. While scanning electron microscopy (SEM) can provide high resolution twodimensional images, atomic force microscopy (AFM) is essential for obtaining three-dimensional information of the defects of interest (DOI) as it is known to provide the highest vertical resolution among all techniques [1]. However AFM’s low throughput, limited tip life, and laborious efforts for locating the DOI have been the limitations of this technique for defect review for 300 mm wafers. To address these limitations of AFM, automatic defect review AFM has been introduced recently [2], and is utilized in this work for studying DOI on 300 mm silicon wafer. In this work, we carefully etched a 300 mm silicon wafer with a gaseous acid in a reducing atmosphere at a temperature and for a sufficient duration to decorate and grow the crystal defects to a size capable of being detected as light scattering defects [3]. The etched defects form a shallow structure and their distribution and relative size are inspected by laser light scattering (LLS). However, several groups of defects couldn’t be properly sized by the LLS due to the very shallow depth and low light scattering. Likewise, SEM cannot be used effectively for post-inspection defect review and classification of these very shallow types of defects. To verify and obtain accurate shape and three-dimensional information of those defects, automatic defect review AFM (ADR AFM) is utilized for accurate locating and imaging of DOI. In ADR AFM, non-contact mode imaging is used for non-destructive characterization and preserving tip sharpness for data repeatability and reproducibility. Locating DOI and imaging are performed automatically with a throughput of many defects per hour. Topography images of DOI has been collected and compared with SEM images. The ADR AFM has been shown as a non-destructive metrology tool for defect review and obtaining three-dimensional topography information.
Defects on a reticle are inspected, reviewed, and repaired by different tools. They are located by automated optical inspection (AOI); however, if the characteristic size of defects is similar to that of light and electron beam wavelengths, they are often unclassified or misclassified by AOI. Atomic force microscopes (AFM) along with electron microscopes are used for investigating defects located by AOI to distinguish false defects from real defects and effectively classify them. Both AFM and electron microscopes provide high resolution images. However, electron microscopy is known to be destructive and have less accuracy in 3rd dimension measurement compared to AFM [1]. On the other hand, AFM is known to have low throughput and limited tip life in addition to requiring significant effort to finding the defects. These limitations emanate from having to perform multiple large scans to find the defect locations, to compensate for stage coordinate inaccuracies, and to correct the mismatch between the AFM and the AOI tools.
In this work we introduce automatic defect review (ADR) AFM for defect study and classification of EUV mask reticles that overcomes the aforementioned limitations of traditional AFM. This metrology solution is based on an AFM configuration with decoupled Z and XY scanners that makes it possible to collect large survey images with minimum out of plane motion. To minimize the stage errors and mismatch between the AFM and the AOI coordinates, the coordinates of fiducial markers are used for coarse alignment. In addition, fine alignment of the coordinates is performed using enhanced optical vision on marks on the reticle. The ADR AFM is used to study a series of phase defects identified by an AOI tool on a reticle. Locating the defects, imaging, and defect classification are performed using the ADR automation software and with the throughput of several defects per hour. In order to preserve tip life and data consistency, AFM imaging is performed in non-contact mode. The ADR AFM provides high throughput, high resolution, and non-destructive means for obtaining 3D information for defect review and classification. Therefore this technology can be used for in-line defect review and classification for mask repair.
While feature size in lithography process continuously becomes smaller, defect sizes on blank wafers become more
comparable to device sizes. Defects with nm-scale characteristic size could be misclassified by automated optical
inspection (AOI) and require post-processing for proper classification. Atomic force microscope (AFM) is known to
provide high lateral and the highest vertical resolution by mechanical probing among all techniques. However, its low
throughput and tip life in addition to the laborious efforts for finding the defects have been the major limitations of this
technique. In this paper we introduce automatic defect review (ADR) AFM as a post-inspection metrology tool for
defect study and classification for 300 mm blank wafers and to overcome the limitations stated above. The ADR AFM
provides high throughput, high resolution, and non-destructive means for obtaining 3D information for nm-scale defect
review and classification.
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