Natural physical phenomena occurring at length scales of a few nm in EUV lithography give rise to variation in photoresist images: edge, width, and top roughness, feature-to-feature CD or shape variability, edge placement errors, etc. The most damaging are stochastic printing failures caused by undesirable film thickness loss, admitting etch in line regions, or film thickness gain, preventing etch in space regions. In this work, we begin from analysis of well-calibrated rigorous physical stochastic EUV lithography models to study nanoscale exposure effects affecting stochastic failures. We apply acceleration to the stochastic model and perform computational inspection and classification of hot spots on a large layout area. The agreement between predicted probabilities of occurrence and observed defect frequencies are given for both line and space hot spots. We then perform computational inspection upon a virtual process and select hot spot locations and affect repairs. The actual mask is then fabricated, real wafers are exposed, processed, inspected and measured to compare the predicted reductions in defect probabilities with actual measured defect frequencies on wafer.
To maintain lithographic pitch scaling, extreme ultraviolet (EUV) processes have been adopted in high-volume manufacturing (HVM) for today’s advanced logic and memory devices. Among various defect sources, stochastic patterning defects are one of the most important yield detractors for EUV processes. In this work, we will limit our scope to patterning defects arising out of lithography. In the past, it has been shown that the patterning defect process window is often limited by stochastic hotspots. These hotspots have very low failure probabilities in a well-optimized process, and hence their detection necessitates large area sensitive defect inspection, such as with a broadband plasma (BBP) optical defect inspection system. It has also been shown that systematic issues in design can be exacerbated by stochastic variations. Hence, it is critical to discover these hotspots and study their variability with massive SEM metrology. Such analyses can uncover systematic trends, which can then be corrected and monitored. In this work, we discover hotspots using broadband plasma (BBP) optical inspection and study their variability using KLA’s aiSIGHT™ pattern-centric defect and metrology software solution for automatic defect classification and SEM metrology measurements. We also demonstrate the need for fast and rigorous 3D probabilistic stochastic defect detection on design as a continuation of this work.
Background: Natural physical phenomena occurring at length scales of a few nm produces variation in many aspects of the EUV photoresist relief image: edge roughness, width roughness, feature-tofeature variability, etc. 1,2,3,4. But the most damaging of these variations are stochastic or probabilistic printing failures 5, 6. Stochastic or probabilistic failures are highly random with respect to count and location and occur on wafers at spectra of unknown frequencies. Examples of these are space bridging, line breaking, missing and merging holes. Each has potential to damage or destroy the device, reducing yield 6, 10. Each has potential to damage or destroy the device, reducing yield 6, 10. The phenomena likely originates during exposure where quantized light and matter interact1 . EUV lithography is especially problematic since the uncertainty of energy absorbed by a volume of resist is much greater at 13.5 nm vs. 248 nm and 193 nm. Methods: In this paper, we use highly accelerated rigorous 3D probabilistic computational lithography and inspection to scan an entire EUV advanced node layout, predicting the location, type and probability of stochastic printing failures.
The link between suspended particle fields, particle dynamics and bulk optical properties in natural waters is poorly
known because adequate technology is lacking to fully characterize critical parameters and interactions, especially for
ephemeral bubbles and aggregates. This paper highlights the capabilities of digital holography to provide non-intrusive,
high-resolution 3-D imaging of particles and bubbles in their natural environment. As part of a NOPP project
(HOLOCAM) to commercialize an in-situ digital holographic microscope (DHM), field data with a prototype in-situ
DHM (the "Holosub") were collected in East Sound, WA. The Holosub, an in-line holography based submersible
platform, was deployed in two configurations: free-drifting mode for vertical profiling, and towed mode. In free-drifting
mode, vertical profiles of shear strain and dissipation rates, undisturbed size and spatial distributions of particles and
organisms, and the orientation of diatom chains were recorded using the holographic images. Hydrographic and optical
data, as well as discrete water samples to identify phytoplankton species were concurrently collected. In towed mode,
the size and spatial distributions of bubbles just below the surface were recorded to characterize the dissipation of a
wake generated by another ship, and compared to optical and acoustic scattering data recorded simultaneously. Tools to
extract the size distribution and concentration of bubbles from the holographic data were developed. A preliminary data
analysis indicated high concentrations of bubbles detected by all three instruments at the same locations, while
comparison of the bubble size distributions indicated some similarities in trends, as well as significant differences.
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