Method for optical proximity correction based on a vector imaging model.

Optical proximity correction (OPC) has become an indispensable step in integrated circuit manufacturing. It requires a huge amount of calculation to obtain a sufficiently accurate OPC model and implement pattern correction. In this paper, the authors proposed an edge-based OPC method built on a vector imaging model, where the analytical correlation between the cost function and movement of each edge segment is established by the chain rule. First, the mask pattern is segmented and downsampled to get the mask image in order to reduce the total data. Second, the aerial image, various parameters on each evaluating point, and the final cost value are obtained in proper sequence. In each part of the OPC process, the procedures of solution and derivation are both recorded. After obtaining the cost value, the chain rule is applied, by which the differential relation between the cost value and movement of each segment is built. According to this differential relation, the next movement of each segment is decided under a quasi-Newton method. All results obtained by the proposed method are compared with results from commercial software. The comparison shows that the proposed OPC method has good OPC accuracy in few iterations.

Screening for Mild Cognitive Impairment with Speech Interaction Based on Virtual Reality and Wearable Devices.

Significant advances in sensor technology and virtual reality (VR) offer new possibilities for early and effective detection of mild cognitive impairment (MCI), and this wealth of data can improve the early detection and monitoring of patients. In this study, we proposed a non-invasive and effective MCI detection protocol based on electroencephalogram (EEG), speech, and digitized cognitive parameters. The EEG data, speech data, and digitized cognitive parameters of 86 participants (44 MCI patients and 42 healthy individuals) were monitored using a wearable EEG device and a VR device during the resting state and task (the VR-based language task we designed). Regarding the features selected under different modality combinations for all language tasks, we performed leave-one-out cross-validation for them using four different classifiers. We then compared the classification performance under multimodal data fusion using features from a single language task, features from all tasks, and using a weighted voting strategy, respectively. The experimental results showed that the collaborative screening of multimodal data yielded the highest classification performance compared to single-modal features. Among them, the SVM classifier using the RBF kernel obtained the best classification results with an accuracy of 87%. The overall classification performance was further improved using a weighted voting strategy with an accuracy of 89.8%, indicating that our proposed method can tap into the cognitive changes of MCI patients. The MCI detection scheme based on EEG, speech, and digital cognitive parameters proposed in this study provides a new direction and support for effective MCI detection, and suggests that VR and wearable devices will be a promising direction for easy-to-perform and effective MCI detection, offering new possibilities for the exploration of VR technology in the field of language cognition.

Classification of mild cognitive impairment based on handwriting dynamics and qEEG.

Subtle changes in fine motor control and quantitative electroencephalography (qEEG) in patients with mild cognitive impairment (MCI) are important in screening for early dementia in primary care populations. In this study, an automated, non-invasive and rapid detection protocol for mild cognitive impairment based on handwriting kinetics and quantitative EEG analysis was proposed, and a classification model based on a dual fusion of feature and decision layers was designed for clinical decision-marking. Seventy-nine volunteers (39 healthy elderly controls and 40 patients with mild cognitive impairment) were recruited for this study, and the handwritten data and the EEG signals were performed using a tablet and MUSE under four designed handwriting tasks. Sixty-eight features were extracted from the EEG and handwriting parameters of each test. Features selected from both models were fused using a late feature fusion strategy with a weighted voting strategy for decision making, and classification accuracy was compared using three different classifiers under handwritten features, EEG features and fused features respectively. The results show that the dual fusion model can further improve the classification accuracy, with the highest classification accuracy for the combined features and the best classification result of 96.3% using SVM with RBF kernel as the base classifier. In addition, this not only supports the greater significance of multimodal data for differentiating MCI, but also tests the feasibility of using the portable EEG headband as a measure of EEG in patients with cognitive impairment.

Compensation of EUV lithography mask blank defect based on an advanced genetic algorithm.

Mask blank defect is one of the most important factors that degrades the image quality of extreme ultraviolet (EUV) lithography system, and further leads to a yield lose. In order to compensate the amplitude and phase distortions caused by the EUV mask blank defects, this paper proposes an advanced algorithm to optimize the mask absorber pattern based on genetic algorithm. First, a successive approximation correction method is used to roughly compensate the effect of mask blank defect. Then, an advanced genetic algorithm is proposed to obtain higher efficiency and compensation accuracy, which uses an adaptive coding strategy and a fitness function considering normalized image log slope of lithography image. For illustration, the proposed method is verified based on rectangular contact patterns and complex pattern with different defects. The aerial images of optimized masks are evaluated by a commercial lithography simulator. It will show that the proposed method can mitigate the impact of mask defects, and improve the fidelity of lithography print image. The simulation results also demonstrate the higher convergence efficiency and mask manufacturability can be guaranteed by the proposed method.

EUV multilayer defect characterization via cycle-consistent learning.

Extreme ultraviolet (EUV) lithography mask defects may cause severe reflectivity deformation and phase shift in advanced nodes, especially like multilayer defects. Geometric parameter characterization is essential for mask defect compensation or repair. In this paper, we propose a machine learning framework to predict the geometric parameters of multilayer defects on EUV mask blanks. With the proposed inception modules and cycle-consistent learning techniques, the framework enables a novel way of defect characterization with high accuracy.

[Effect of Curcuma aromatica on epithelial-myofibroblast transdifferentiation of NRK-52E cells induced by TGF-beta1].

OBJECTIVE: To observe the antagonist effect of Curcuma Aromatica (CA) on renal tubular epithelial-myofibroblast transdifferentiation (EMT) induced by transforming growth factor-beta1 (TGF-beta1). METHODS: Normal renal tubular epithelial NRK-52E cells in vitro cultured were randomly divided into 6 groups, i.e., the normal control group (Group C), the TGF-beta1 induced model group (Group T), the low dose CA treated group (Group E1), the moderate dose CA treated group (Group E2), the high dose CA group (Group E3), and the Benazepril Hydrochloride Tablet treated group (Group Y). Except Group C, corresponding medication (with an action of 48 h) was administered to cells in the rest groups after they were induced by TGF-beta1 for 24 h. The morphological changes were observed by inverted phase contrast microscope. The distribution of beta-actin protein was detected by immunohistochemical assay. The mRNA expressions of alpha-smooth muscle actin (alpha-SMA) and E-cadherin (E-cad) were detected by real-time PCR. The concentration of fibronectin (FN) was detected by ELISA. RESULTS: After induced by TGF-beta1 for three days, hypertrophy and elongated cells in fusiform-shape occurred,with increased expressions of beta-actin protein in the cytoskeletal structure (P < 0.05), bundle fibrous structure occurred inside cytoplasm with significantly up-regulated intracellular alpha-SMA mRNA expressions (P < 0.05), E-cad mRNA expression decreased (P < 0.05), the FN content in the supernate increased (P < 0.05) in Group T. Compared with Group T, partial cells in Group E1, E2, and E3 showed fibrous changes, accompanied with decreased expression of beta-actin protein and FN concentration (P < 0.05). The expression of alpha-SMA mRNA increased and the expression E-cad mRNA decreased in Group E2 and E3 (both P < 0.05). But there was no statistical difference in the expression levels of E-cad and alpha-SMA mRNA (P > 0.05). Compared with Group E1, the expression of beta-actin protein and FN concentration decreased in Group E2 and E3 (P < 0.05). The expressions of alpha-SMA mRNA decreased and E-cad mRNA increased in Group E3 (P < 0.05). Compared with Group Y, the expression of beta-actin mRNA and FN concentration increased in Group E1 (P < 0.05); the expression of beta-actin mRNA increased in Group E3 (P < 0.05); the expression of E-cad mRNA decreased in Group E3 (P < 0.05). There was no statistical difference in the expression of alpha-SMA mRNA among Group E1, E2, and E3 (P > 0.05). CONCLUSION: CA could inhibit the occurrence of TGF-beta1 induced EMT, which could be used as an effective drug for treating chronic renal insufficiency.