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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.
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