Dr. Curtis Qiang Liang Profile

Dr. Curtis Qiang Liang

Senior Marketing Manager at ASML Intl Trading Co Ltd

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Publications (4)

Proceedings Article | 9 June 2006 Paper

Improving model-based optical proximity correction accuracy using improved process data generation

Mark Lu, Dion King, Curtis Liang, Lawrence Melvin

Proceedings Volume 6149, 61490U (2006) https://doi.org/10.1117/12.674217

KEYWORDS: Photomasks, Data modeling, Optical proximity correction, Model-based design, Process modeling, Reticles, Inspection, Data processing, Critical dimension metrology, Semiconducting wafers

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Proceedings Article | 24 March 2006 Paper

Feedback model evaluation of high-mix product manufacturing

Dion King, Mingjen Cheng, Aho Lu, Zhibiao Mao, Curtis Liang

Proceedings Volume 6152, 615242 (2006) https://doi.org/10.1117/12.655863

KEYWORDS: Data modeling, Overlay metrology, Process control, Data processing, Neural networks, Instrument modeling, Erbium, Manufacturing, Signal processing, Performance modeling

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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.

Proceedings Article | 14 March 2006 Paper

Lithography window check before mask tape-out in sub-0.18um technology

Mark Lu, Dion King, Flora Li, Zhibiao Mao, Curtis Liang, Lawrence Melvin

Proceedings Volume 6156, 61560X (2006) https://doi.org/10.1117/12.655851

KEYWORDS: Photomasks, Process modeling, Lithography, Oxides, Optical proximity correction, Manufacturing, Optical lithography, Resistance, Scanning electron microscopy, Semiconductors

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Proceedings Article | 9 November 2005 Paper

Off-target model based OPC

Mark Lu, Curtis Liang, Dion King, Lawrence Melvin III

Proceedings Volume 5992, 599257 (2005) https://doi.org/10.1117/12.633116

KEYWORDS: Data modeling, Model-based design, Optical proximity correction, Process modeling, Semiconducting wafers, Data processing, Thin films, Photomasks, Light wave propagation, Manufacturing

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