the key of novel

最新推荐文章于 2025-10-10 23:45:00 发布
最新推荐文章于 2025-10-10 23:45:00 发布 · 90 阅读 · 0

此博客提及不同题材小说的创作要点，科幻题材需有悬疑成分，仅直白幻想不够；仙侠小说要写出灵气、侠气和霸气；言情小说则要具备幽默元素。

科幻题材小说，一定要有悬疑的成分。直白的表述自己的幻想，不够。看科幻的人，好奇心很强。

仙侠小说，要写出灵气，侠气，霸气。

言情小说，要幽默。

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Abstract—Semiconductor fabrication is regarded as one of the most complicated production processes with a high-mixed and uncertain production context, and it suffers from tool failures. Prognostics can predict the time when a process tool cannot perform its intended function. Therefore, preventive maintenance can be planned in time with the minimum impact on the production lines. Because most of the tool faults cannot be owed to the failure of a single component, it is challenging to model the system directly through mechanism analysis. Therefore, the development of failure prognostics in semiconductor manufacturing is somewhat impeded. Data-driven approaches are appropriate in this case, especially when the domain knowledge of the system under study is not comprehensive enough. This article attempts to propose a novel data-driven prognostic method integrated with the auto-associative regression and the Gaussian process to address the issue. This proposed method can extract the failure factors, establish the prognostic model and present the reliability of the model. On the basis of the built prognostic model, the failure tendency is predicted and the maintenance schedule can be determined. The validity and feasibility of the proposed method are demonstrated through a numerical example and a practical semiconductor manufacturing process, respectively. The proposed prognostic scheme can guide the frequency plan of preventive maintenance in the fabrication process and improve the productivity in semiconductor manufacturing. Index Terms—Auto-associative regression (AR), Gaussian process (GP), prognostic, semiconductor fabrication, tool failure
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The abstract discusses a novel data-driven approach combining auto-associative regression and Gaussian processes for predicting tool failures in semiconductor manufacturing. The user likely wants this...
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