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Pattern type specific modeling and correction methodology at high NA and off-axis illumination

机译:在高NA和偏轴照明下特定于图案类型的建模和校正方法

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In this work, a potential drawback of simultaneously representing a set of data that contains line-ends, isobar, block structure, and pitch linearity intensity signal using a single representative model have been resolved. In a typical model OPC procedure, a set of pattern data representative of OPC layout is calibrated using a single representative model, and this model may be a scalar or a vector at constant threshold or variable threshold. Nevertheless, traditional methodology treats a set of pattern data as a whole believing that it provides a best representation of a more complicated environment. In this study, pattern type specific models are used to perform optical proximity correction. This multi-model approach distinguishes each pattern type and specified pitch range a priori to obtaining intensity signal by checking for neighboring segment. Based on this search result, its segment is classified into a pattern type and subgroup, and then, pattern specific models are applied. This approach provides improved calibration result for strong off-axis illumination and optical proximity correction result which will be difficult to achieve with a single representative model.
机译:在这项工作中,解决了使用单个代表模型同时表示一组包含线端,等压线,块结构和音高线性强度信号的数据的潜在缺点。在典型的模型OPC程序中,使用单个代表模型校准代表OPC布局的一组图案数据,并且该模型可以是标量或恒定阈值或可变阈值的向量。尽管如此,传统方法论认为将一组模式数据作为一个整体来对待,认为它可以最好地表示更复杂的环境。在这项研究中,特定于图案类型的模型用于执行光学邻近校正。这种多模型方法可通过检查相邻段来区分每种模式类型和指定的音高范围,从而获得强度信号。基于此搜索结果,将其片段分为模式类型和子组,然后应用模式特定的模型。这种方法为强离轴照明和光学接近度校正结果提供了改进的校准结果,而使用单个代表性模型很难实现这一点。

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