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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.

Abstract Purpose With the in-depth application of machine learning(ML) in clinical practice, it has been used to predict the mortality risk in patients with traumatic brain injuries(TBI). However, there are disputes over its predictive accuracy. Therefore, we implemented this systematic review and meta-analysis, to explore the predictive value of ML for TBI. Methodology We systematically retrieved literature published in PubMed, Embase.com, Cochrane, and Web of Science as of November 27, 2022. The prediction model risk of bias(ROB) assessment tool (PROBAST) was used to assess the ROB of models and the applicability of reviewed questions. The random-effects model was adopted for the meta-analysis of the C-index and accuracy of ML models, and a bivariate mixed-effects model for the meta-analysis of the sensitivity and specificity. Result A total of 47 papers were eligible, including 156 model, with 122 newly developed ML models and 34 clinically recommended mature tools. There were 98 ML models predicting the in-hospital mortality in patients with TBI; the pooled C-index, sensitivity, and specificity were 0.86 (95% CI: 0.84, 0.87), 0.79 (95% CI: 0.75, 0.82), and 0.89 (95% CI: 0.86, 0.92), respectively. There were 24 ML models predicting the out-of-hospital mortality; the pooled C-index, sensitivity, and specificity were 0.83 (95% CI: 0.81, 0.85), 0.74 (95% CI: 0.67, 0.81), and 0.75 (95% CI: 0.66, 0.82), respectively. According to multivariate analysis, GCS score, age, CT classification, pupil size/light reflex, glucose, and systolic blood pressure (SBP) exerted the greatest impact on the model performance. Conclusion According to the systematic review and meta-analysis, ML models are relatively accurate in predicting the mortality of TBI. A single model often outperforms traditional scoring tools, but the pooled accuracy of models is close to that of traditional scoring tools. The key factors related to model performance include the accepted clinical variables of TBI and the use of CT imaging.

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.

Due to the unique properties such as nontoxicity, biodegradability, availability from renewable resources, and cost-effectiveness, polysaccharides play a very important part in the science and technology field. The various chemically modified derivatives of these offer a wide range of high value-added in both food and non-food industries. Among the chemical modification, etherified polysaccharide is one of the most widespread derivatives by introducing an ether group which is commonly stable in both acidic and alkaline conditions. Hydroxyalkylation, alkylation, carboxymethylation, cationization, and cyanoethylation are some of the modifications commonly employed to prepare polysaccharides ethers derivatives. There also has been a growing tendency for creating new types of modification by combining the different means of chemical techniques. The correct determination of degree of substitution (DS)/molar substitution (MS) is crucially important. The objective of this article is to summarize developments in synthetic etherified polysaccharides, involving analytical methods for determination of MS/DS, measurement processes, and the associated mechanisms.