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Abstract P91 steel is an important steam pipe for ultra-supercritical power plants due to its excellent creep strength, which generally has a design life of 100,000 h. Here, we found a significant aberrant decrease in the creep rupture life of a main steam pipe elbow after only 20,000 h of service. The microstructure in the aberrant piece exhibited a decomposition of martensitic lath into blocky ferrite due to recrystallization and accumulation of M23C6 as well as formation of the Laves phase along the prior austenitic grain boundaries, resulting in the decrease of hardness that no long meet ASME standard requirement. The creep testing of the P91 piece at 550–600 °C and 85–140 MPa shows that the influence of temperature on the cavity formation and cracking is greater than that of the applied stress. The rupture life is nearly two orders of magnitude shorter than the normal P91, attributing to the creep damage of the subgrain growth, M23C6 and Laves phase coarsening (aggregation approaching 3.4 μm). The residual life of the aberrant piece was evaluated to be 53,353 h based on the Larson–Miller parameter, which is much shorter than the design life, suggesting the safety operation of the elbow area should be paid more attention during the afterward service periods. P91 steel, main steam pipe elbow, aberrant microstructure, service degradation, creep life prediction

In semiconductor fabrication, any deviation leads to significant mistakes in the result. Thus, the proximity effect is a critical issue that must be solved. In the past, optical proximity correction was constructed by fabrication experience and physics formula models, resulting in difficulties when the technology node shrinks. As a result, optical proximity correction with machine learning models is highly expected to solve the issue in recent years. Due to the unique feature in optical proximity correction, single-flow convolutional feedback networks with customized attention layer are proposed to compete with widely used U-Net or U-Net with attention layer, which is the current mainstream in image-to-image machine learning tasks. The customized attention layer is used to replace the conventional attention layer. The proposed model with a customized attention layer has improved metrics compared to U-Net or U-Net with an attention layer. Compared the proposed model to U-Net with a cross-attention layer, we observe 3.74% improvement of modified mean pixel accuracy in the two-bar dataset, 0.9% improvement of modified mean pixel accuracy in the tri-bar dataset, 3.76% improvement of modified mean pixel accuracy in the polygon dataset and 2.06% improvement of modified mean pixel accuracy in the GAN400 dataset. The code is available at https://github.com/albertlin11/OPCfb.

Reticle optimization is a computationally demanding task in optical microlithography for advanced semiconductor fabrication. In this study, we explore the effectiveness of D-Wave’s quantum annealing (QA) and hybrid steepest descent (SD) solvers in solving pixelated binary reticle optimization problems. We show that the energy derived from the objective function depends on annealing time and inter-sample correlation. Specifically, longer annealing times and reduced inter-sample correlations result in lower energy. Moreover, introducing efficient pausing strategies in forward annealing could reduce the QA runtime by approximately 100-fold while achieving similar results to long annealing times. Finally, reticles with increased variables lead to widespread irregular values in default sorted QA energies due to quantum chain breakages, which could potentially limit the probability of attaining the optimal solution. A hybrid approach that applies the classical SD algorithm to the QA results increases the probability of locating the global minimum solution and reduces runtime to about one-third compared to the classical SD solver. These findings facilitate our comprehension of quantum computing for accelerating computational lithography in semiconductor manufacturing.

In the context of global warming, the accelerated degradation of circum-Arctic permafrost is releasing a significant amount of carbon. InSAR can indirectly reflect the degradation of permafrost by monitoring its deformation. This study selected three typical permafrost regions in North America: Alaskan North Slope, Northern Great Bear Lake, and Southern Angikuni Lake. These regions encompass a range of permafrost landscapes, from tundra to needleleaf forests and lichen-moss, and we used Sentinel-1 SAR data from 2018 to 2021 to determine their deformation. In the InSAR process, due to the prolonged snow cover in the circum-Arctic permafrost, we used only SAR data collected during the summer and applied a two-stage interferogram selection strategy to mitigate the resulting temporal decorrelation. The Alaskan North Slope showed pronounced subsidence along the coastal alluvial plains and uplift in areas with drained thermokarst lake basins. Northern Great Bear Lake, which was impacted by wildfires, exhibited accelerated subsidence rates, revealing the profound and lasting impact of wildfires on permafrost degradation. Southern Angikuni Lake’s lichen and moss terrains displayed mild subsidence. Our InSAR results indicate that more than one-third of the permafrost in the North American study area is degrading and that permafrost in diverse landscapes has different deformation patterns. When monitoring the degradation of large-scale permafrost, it is crucial to consider the unique characteristics of each landscape.

The thermal and photochemical stabilities of colloidal quantum-dot (QD) nanophosphors incorporated into a blue microLED matrix by spin-cast deposition were investigated in this study. The optical characteristics of QDs-based color-converted microLEDs were also investigated in this study. The measurements performed in this work suggest the ultimate limit of QD performance in microLED devices. The findings from the stability evaluations encourage and motivate the employment of red, green QD color converters in blue microLED matrices to create full-color microLED devices. Such studies will be helpful in the design and the fabrication of high-efficiency and high-performance micro-LED microdisplay panels

Optical proximity correction (OPC) is a critical step in semiconductor manufacturing due to its high complexity and significant influence on the subsequent process steps. Conventional OPC using the Maxwell equation can become more and more challenging as a fully vectorized three-dimensional simulation is required for advanced technology nodes. Machine learning (ML) has been a promising alternative recently. This work proposes machine-learning-assisted human decision, which can be more in line with the clean room engineer's practice and can potentially surpass pure human decision and the pure machine learning approach. Using 10-step optimization in the photolithographic mask, the averaged mean square error (MSE) at the optimized cases are 6360 and 2101 for two-bar patterns and 7132 and 5931 for tri-line attackers when comparing pure human decision and ML-assisted human. The average MSEs at the first 3 steps are 26019 and 6023 for the two-bar pattern and 79979 and 7738 for the tri-line attacker when comparing pure ML and ML-assisted human. It is suggested that the strength of the ML-assisted human decision lies in the early-stage superiority over pure ML, flexibility, incorporating past experience, and a human sense that cannot be formulated concretely by statistical models.

Herein, we describe a one-step method for synthesizing cationic acrylate-based core-shell latex (CACS latex), which is used to prepare architectural coatings with excellent antimicrobial properties. Firstly, a polymerizable water-soluble quaternary ammonium salt (QAS-BN) was synthesized using 2-(Dimethylamine) ethyl methacrylate (DMAEMA) and benzyl bromide by the Hoffman alkylation reaction. Then QAS-BN, butyl acrylate (BA), methyl methacrylate (MMA), and vinyltriethoxysilane (VTES) as reactants and 2,2’-azobis(2-methylpropionamidine) dihydrochloride (AIBA) as a water-soluble initiator were used to synthesize the CACS latex. The effect of the QAS-BN dosage on the properties of the emulsion and latex film was systematically investigated. The TGA results showed that using QAS-BN reduced the latex film’s initial degradation temperature but improved its thermal stability. In the transmission electron microscopy (TEM) photographs, the self-stratification of latex particles with a high dosage of QAS-BN was observed, forming a core-shell structure of latex particles. The DSC, TGA, XPS, SEM, and performance tests confirmed the core-shell structure of the latex particles. The relationship between the formation of the core-shell structure and the content of QAS-BN was proved. The formation of the core-shell structure was due to the preferential reaction of water-soluble monomers in the aqueous phase, which led to the aggregation of hydrophilic groups, resulting in the formation of soft-core and hard-shell latex particles. However, the water resistance of the films formed by CACS latex was greatly reduced. We introduced a p-chloromethyl styrene and n-hexane diamine (p-CMS/EDA) crosslinking system, effectively improving the water resistance in this study. Finally, the antimicrobial coating was prepared with a CACS emulsion of 7 wt.% QAS-BN and 2 wt.% p-CMS/EDA. The antibacterial activity rates of this antimicrobial coating against E. coli and S. aureus were 99.99%. The antiviral activity rates against H3N2, HCoV-229E, and EV71 were 99.4%, 99.2%, and 97.9%, respectively. This study provides a novel idea for the morphological design of latex particles. A new architectural coating with broad-spectrum antimicrobial properties was obtained, which has important public health and safety applications.