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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：随着模式越来越小，控制边缘紧密过程的难度是指数级膨胀的。因此，使用自动化过程控制（APC）在光刻过程中被广泛采用的平均值，以控制覆盖层和CD变化。 APC的准确性取决于先前的过程数据的量。然而，在具有高混合产品的铸造厂中，典型的历史数据点对于低批量产品的工艺参数来说是有足够的。结果是导频运行的高返工率和由于工艺参数预测较差的覆盖而导致的测试符号。已经报道了对预测覆盖校正的方法的几项研究。构建良好预测模型的关键是将覆盖物错误分解为几个部分。有些是设备或技术相关错误，由所有产品共享。其他是某些产品的特征，例如掩码错误或特殊对准标记。在生产环境中，前部件通过处理各种产品的数据反馈实时更新。低批量产品或试验产品可以共享信息。因此，我们可以为新产品达到更准确的控制或预测。在本文中，我们提供了一种新模型，用于预测用于导频运行的覆盖物的过程参数设置，或者在长时间内未在工具上运行的产品。该新模型是简化的小脑操作算术控制器（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是SCMAC中的一个单元格，并通过使用此假设构建整个小区表。最终覆盖校正值是一组小区的总和，其被一批信息激活。我们还将介绍这一新SCMAC模型的建筑和培训细节。还评估了SCMAC在覆盖参数中的预测准确性。根据结果​​，SCMAC可以成功地将叠加误差分成几个因素，并还克服设备和过程中的不匹配。我们还将新的SCMAC模型与一般指数加权移动平均（EWMA）模型进行了比较，其基于历史数据点计算校正值，并且在该数据点中的计算中具有更多重量。基于结果，SCMAC模型不足以替代EWMA模型控制高批量产品的覆盖层。

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