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首页> 外文会议>Metrology, Inspection, and Process Control for Microlithography XX pt.2 >Feedback Model Evaluation of High-mix Product Manufacturing
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Feedback Model Evaluation of High-mix Product Manufacturing

机译：高混合产品制造的反馈模型评估

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As the patterns are getting smaller, the difficulty to control a margin-tight process expands exponentially. The use of the Automated Process Control (APC), therefore, becomes a widely employed mean in photolithography process to control overlay and CD variations. The accuracy of APC is dependent upon the amount of the previous process data. However, in a foundry with high-mix products it is typical that there are not enough historic data points for accurate calculation of process parameters for a low volume product. The consequence is the high rework rate of pilot runs and test runes due to poor process parameter prediction for overlay. Several studies of the method for predicting the overlay correction have been reported. The key to build a good prediction model is to break the overlay errors down to several parts. Some are equipment or technology related errors, which are shared by all products. Others are the characteristic for certain products, for instance, mask error or special alignment marks. In the production environment the former parts are updated in real time by data feedback from processing all kinds of products. The low volume products or pilot products can share the information. Thus we can achieve a more accurate control or prediction for a new product. In this paper we provide a new model for predicting the process parameter settings of overlay for a pilot run or a product not being run on a tool for a long period of time. This new model is a Simplified Cerebellar Manipulation Arithmetic Controller (SCMAC), which is one kind of Neural Network (NN) model. We assume each part of overlay errors is a cell in SCMAC and build the whole cell table by using this assumption. The final overlay correction value is the sum of a group of cells, which is activated by one lot information. We will also present the details of the building and training of this new SCMAC model. The prediction accuracy of SCMAC in overlay parameters is also evaluated. According to the results, SCMAC can split the overlay error to several factors successfully and also overcome the mismatch in the equipments and processes. We also compare the new SCMAC model with the general Exponential Weighted Moving Average (EWMA) model, which calculates the correction value based on the history data points, and in which the newer data points have more weight in the calculation. Based on the results, the SCMAC model is not good enough to substitute the EWMA model in controlling the overlay of a high volume product.
机译：随着图案越来越小，控制边距紧密过程的难度呈指数增长。因此，自动过程控制（APC）的使用已成为光刻工艺中控制覆盖和CD变化的一种广泛采用的手段。 APC的准确性取决于先前过程数据的数量。但是，在具有高混合产品的铸造厂中，通常没有足够的历史数据点来准确计算小批量产品的工艺参数。结果是由于覆盖的过程参数预测不佳而导致中试和测试符文的返工率很高。已经报道了一些预测覆盖校正的方法的研究。建立良好的预测模型的关键是将覆盖误差分解为几个部分。有些是与设备或技术相关的错误，所有产品均会共享。其他是某些产品的特征，例如，掩膜错误或特殊的对准标记。在生产环境中，通过处理各种产品的数据反馈实时更新以前的零件。小批量产品或中试产品可以共享信息。因此，我们可以对新产品进行更准确的控制或预测。在本文中，我们提供了一个新模型，用于预测试运行或产品长时间不在工具上运行的覆盖过程参数设置。该新模型是简化的小脑操纵算术控制器（SCMAC），它是一种神经网络（NN）模型。我们假设覆盖错误的每个部分都是SCMAC中的一个单元，并使用此假设构建整个单元表。最终的叠加校正值是一组单元格的总和，该总和由一个批次信息激活。我们还将介绍此新SCMAC模型的构建和培训的详细信息。还评估了SCMAC在覆盖参数中的预测精度。根据结果​​，SCMAC可以将覆盖误差成功地分为几个因素，并且还可以克服设备和过程中的不匹配。我们还将新的SCMAC模型与常规的指数加权移动平均值（EWMA）模型进行了比较，后者基于历史数据点计算校正值，而更新的数据点在计算中具有更大的权重。根据结果​​，SCMAC模型不足以代替EWMA模型来控制大批量产品的覆盖。

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