Deep learning denoising of SEM images towards noise-reduced LER measurements

Overlay control has been one of the most critical issues for manufacturing of leading edge semiconductor devices. Introduction of the double patterning process requires stringent overlay control. Conventional optical overlay (Opt-OL) metrology has technical challenges with measurement robustness, solving overlay discrepancy between overlay mark and device pattern, and measuring smaller marks laid out in large numbers within the die accurately for high-order correction. In contrast, scanning electron microscope-based overlay (SEM-OL) metrology can directly measure both overlay targets and actual devices or device-like structures on processed wafers with high spatial resolution. It can be used for reference metrology and optimization of Opt-OL measurement conditions. SEM-OL uses small structures, including actual device patterns, which allows insertion of many SEM-OL targets across a die. Precise overlay distribution can be measured using dedicated SEM-OL mark, improving measurement accuracy and repeatability. To extend SEM-OL capability, we have been developing SEM-OL techniques that can measure not only surface patterns by critical dimension SEM but also buried patterns for leading edge device processes. There are two techniques to detect buried patterns. One is to use high-acceleration voltage SEM, which detects backscattering electron emphasizing material contrast. It has been adopted for overlay measurements for memory and logic devices at after-etch inspection or even after-develop inspection. The other is to utilize charging effect, which reflects voltage contrast at the surface depending on the material properties of underneath structure. SEM-OL measurement using transient voltage contrast has been developed and its capability of overlay measurement has been proven. An overlay measurement algorithm using template matching method has been developed and was applied to dynamic random access memory (DRAM) process monitor in manufacturing. In order to extend SEM-OL metrology to beyond 3-nm node logic and cutting-edge DRAM devices (half pitch = 14 nm), we are improving measurement precision of detecting buried patterns and measurement throughput by developing optimized SEM-OL mark.

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Review of scanning electron microscope-based overlay measurement beyond 3-nm node device

The predictive power of computational lithography is often demonstrated by showing predicted 2D pattern shapes
compared with top-down SEM images. However, image formation in a SEM is a complex process [1,2,3], and for most
3D lithography and OPC simulators, line width measurements and 2D pattern shapes are based on extracted resist
polygons at a fixed height above the substrate. Generating resist polygon shapes with this method is driven by
computationally efficiency instead of an attempt to describe the image formation process in an actual SEM. We present
PROLITH photolithography simulations combined with simulation of the CD SEM to investigate the interactions
between lithography and metrology. Our CD SEM simulator is a simplification of the complicated image formation
process [4], but it captures many effects seen experimentally. For example, narrow trenches and contact holes are dark
at the bottom in our simulated SEM images, while for isolated lines, the sidewall of the photoresist can clearly be
observed all the way to the resist foot at the substrate. This simple result has important implications when evaluating
lithographic phenomena such as LWR: for polygon-based metrology, simulated LWR is approximately constant with
resist thickness; by contrast, the LWR increases with decreasing thickness when the same simulated 3D resist profiles are
evaluated with the CD SEM simulator.

Investigation of interactions between metrology and lithography with a CD SEM simulator

The relation between detector geometry and image contrast was studied using the Monte Carlo simulator, CHARIOT.
The simulator is capable of modeling electron scattering in the specimen, but it can also model the electron trajectories
outside the specimen under 3-D electric and magnetic fields as well as the detector energy response. SEM images of 10
nm thick structures on a flat substrate were examined. The results demonstrate that the image contrast changes more
drastically by changing the angular window of the detector, rather than by changing the energy response function of the
detector. Particularly, it is shown to be possible to optimize the image contrast and the contrast-to-noise ratio of the
SEM image. The information obtained is useful for designing the SEM detector for specific applications.

Optimizing the detector configuration for SEM topographic contrast by using a Monte Carlo simulation

The interpretation of scanning electron microscopy (SEM) images of the latest semiconductor devices is not intuitive and requires comparison with computed images based on theoretical modeling and simulations. For quantitative image prediction and geometrical reconstruction of the specimen structure, the accuracy of the physical model is essential. In this paper, we review the current models of electron-solid interaction and discuss their accuracy. We perform the comparison of the simulated results with our experiments of SEM overlay of under-layer, grain imaging of copper interconnect, and hole bottom visualization by angular selective detectors, and show that our model well reproduces the experimental results. Remaining issues for quantitative simulation are also discussed, including the accuracy of the charge dynamics, treatment of beam skirt, and explosive increase in computing time.

Modeling of electron-specimen interaction in scanning electron microscope for e-beam metrology and inspection: challenges and perspectives

We are reporting the development of a simulation tool with unique capabilities to comprehensively model an
SEM signal. This includes electron scattering, charging, and detector settings, as well as modeling of the local and
global electromagnetic fields and the electron trajectories in these fields. Experimental and simulated results were
compared for SEM imaging of carbon nanofibers embedded into bulk material in the presence of significant charging,
as well as for samples with applied potential on metal electrodes. The effect of the potentials applied to electrodes on
the secondary emission was studied; the resulting SEM images were simulated. The image contrast depends strongly
on the sign and the value of the potential. SEM imaging of nanofibers embedded into silicon dioxide resulted in the
considerable change of the appeared dimensions of the fibers and as well as tone reversal when the beam voltage was
varied. The results of the simulations are in agreement with experimental results.

Comprehensive simulation of SEM images taking into account local and global electromagnetic fields

Monte Carlo-based SEM image simulation can reproduce SEM micrographs by calculating scattering events of primary electrons inside the target materials. By using the simulated SEM images, it is possible to optimize imaging conditions prior to the specimen observation, which could save time for finding suitable observation condition. However, a recent trend of miniaturized and 3-dimentional structures of semiconductor devices, and introduction of various novel materials have created a challenge for such SEM image simulation techniques; that is, more precise and accurate modeling is required. In this paper, we present a quantitatively accurate BSE simulation and a precise parameters setting in voltage contrast simulation, for both to reproduce experimental SEM images accurately. We apply these simulation techniques to optimize the accelerating voltage of SEM for sub-surface imaging, and to analyze a charge distribution on the insulating specimen under the electron irradiation. These applications promise the advancement in developing a new device by preparing inspecting condition in a timely manner.

SEM image prediction based on modeling of electron-solid interaction

Device miniaturization and complication is now stimulating the strong needs for the three dimensional (3D) geometry measurement of the structure. Now in the single-nanometer nodes, the CDs need to be known at multiple pattern heights especially in after-etch inspection. This means SEM measurements is expected to provide 3D contours. Unfortunately, CD-SEM is basically top-down imaging tool, therefore the acquired micrograph is the 2D projection of the device structure. Meanwhile, optical CD (OCD) works well to deduce the 3D geometrical parameters. Due to the spot size limitation, however, it is hard to obtain the local variation of the structure, or hard to access in-die metrology data. While TEM or slice and view by FIB-SEM is a reliable option to see the local 3D structure, they are destructive and low in statistics due to the long turn-around time. Therefore the challenge is, how to obtain in-die 3D geometrical information in non-destructive way.
The purpose of the present work is to extend CD-SEM capability to extract 3D geometry. The most intuitive approach is the direct imaging, where the structure can be extracted directly from the acquired image. The direct imaging includes, beam tilt SEM and topography detector. While the beam tilt is an intuitive method, at larger tilt over 10 degrees the image blur is unavoidable and the precise metrology application is difficult unless the small tilt angle is enough to see the side wall structure like high-aspect-ratio (HAR) structure over several microns in depth. Topography detector is another well-known approach, and works well for the isolated target like particles or pattern defects. In the smallest spacing, however, characteristic shadow is not created, and the topography extraction is basically difficult. By these reasons, we especially have a strong interest in indirect methods described below.
Indirect approach includes model-based geometry matching and hybrid metrology with other reference measurements. In these approaches, it is required to find the characteristic SEM waveform or signal intensity (gray-level) which are sensitive to the target geometry. The sensitivity of the response can be estimated by the simulation models, or the comparison with other 3D measurements tools like OCD or AFM. The concept is the generalization of the model-based library (MBL), where the SEM waveform is compared with pre-calculated library of the waveforms. In our approach, calibration could be done by using simulation data or real 3D profiling data. Another generalization is that a SEM operation condition is also altered to enhance the sensitivity of all the geometrical parameter. For example, by sweeping the beam voltage, the multiple waveforms are obtained and the low voltage data has a sensitivity in the pattern top geometry, and the higher voltage data in the pattern bottom. Similar parameter sweep can be applied to energy-filter voltages or focus heights. It is expected that this sort of the generalization enhances the correctness of the geometry profiles. 
In the conference presentation, we will compare these 3D measurement approaches and discuss how to realize the reliable in-die 3D measurement in the coming advanced technology nodes.

Makoto Suzuki

Papers

Optimizing the detector configuration for SEM topographic contrast by using a Monte Carlo simulation

Modeling of electron-specimen interaction in scanning electron microscope for e-beam metrology and inspection: challenges and perspectives

Comprehensive simulation of SEM images taking into account local and global electromagnetic fields

SEM image prediction based on modeling of electron-solid interaction

3D-SEM challenges: How can we profile in-die 3D geometry of the integrated circuits? (Conference Presentation)