Pellicles that satisfy transmission, emission, thermal, and mechanical requirements are highly desired for EUV high volume manufacturing. We present here the capability of integrating pellicles in the full flow of rigorous EUV lithography simulations. This platform allows us to investigate new coherence effects in EUV lithography when pellicle is used. Critical dimension uniformity and throughput loss due to pellicle defects and add-on particles are also analyzed. Our study provides theoretical insights into pellicle development and facilitates pellicle insertion in EUV lithography.
Abstract. Resolution and sidelobe are mutual restrict for SAR image. Usually sidelobe suppression is based on resolution reduction. This paper provide a method for resolution enchancement using sidelobe opposition speciality of hanning window and SAR image. The method can keep high resolution on the condition of sidelobe suppression. Compare to traditional method, this method can enchance 50 % resolution when sidelobe is −30dB.
This paper proposes an Adaptive Surface Connectivity Path Planning (ASCPP) algorithm to solve the problem of robots and other autonomous navigation systems moving efficiently and safely through complex environments.The ASCPP algorithm addresses the limitations of existing methods by intelligently leveraging the connectivity of obstacle surfaces.The algorithm is designed to handle a wide range of obstacle shapes and applies to both two-dimensional and three-dimensional environments.The paper first proves that in a two-dimensional Euclidean space, if obstacles are surface-connected, the remaining space will remain connected.The authors also provide an algorithm to find an unobstructed path between any two points in the remaining space.Furthermore, the authors prove that even when surface-connected obstacles are attached to the boundary of a finitely connected Euclidean subspace, the remaining space will still be connected as long as the nonadhered parts of the obstacle's surface and other obstacles non-adhered surfaces remain connected.The ASCPP algorithm operates by adaptively connecting the vertices of obstacles to the start and goal positions, generating a graph representation of the environment.This graph representation allows for efficient exploration and path optimization using graph search techniques.The algorithm also takes into account the geometric properties of obstacles, such as convexity and concavity, to improve path selection and avoid potential collisions.
With the help of statistical software R, EGB2 distribution and JSUo distribution are used to fit the test results of higher mathematics, two GAMLSS models are established, and the rationality and coefficients of the models are tested. GAMLSS model not only quantifies the impact of ordinary learning on final test scores, but also provides timely feedback and control for students'learning, and provides a basis for teachers to reflect on the teaching process and improve teaching methods in time.
Abstract Background Bio-entity Coreference Resolution (CR) is a vital task in biomedical text mining. An important issue in CR is the differential representation of identical mentions as their similar representations may make the coreference more puzzling. However, when extracting features, existing neural network-based models may bring additional noise to the distinction of identical mentions since they tend to get similar or even identical feature representations. Methods We propose a context-aware feature attention model to distinguish similar or identical text units effectively for better resolving coreference. The new model can represent the identical mentions based on different contexts by adaptively exploiting features, which enables the model reduce the text noise and capture the semantic information effectively. Results The experimental results show that the proposed model brings significant improvements on most of the baseline for coreference resolution and mention detection on the BioNLP dataset and CRAFT-CR dataset. The empirical studies further demonstrate its superior performance on the differential representation and coreferential link of identical mentions. Conclusions Identical mentions impose difficulties on the current methods of Bio-entity coreference resolution. Thus, we propose the context-aware feature attention model to better distinguish identical mentions and achieve superior performance on both coreference resolution and mention detection, which will further improve the performance of the downstream tasks.
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