Dr. Chien-Wen Lai Profile

Dr. Chien-Wen Lai

Section Manager at Taiwan Semiconductor Manufaturing Co Ltd

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Publications (4)

Proceedings Article | 7 March 2008 Paper

Pellicle effect on OPC modeling

Boren Luo, Chi-Kang Chang, W. Wang, W. Huang, Timothy Wu, C. Lai, R. Liu, H. Lin, K. Chen, Y. Ku

Proceedings Volume 6924, 69243T (2008) https://doi.org/10.1117/12.775419

KEYWORDS: Pellicles, Optical proximity correction, Photomasks, Data modeling, Polarization, Immersion lithography, Projection systems, Diffraction, Signal attenuation, Critical dimension metrology

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Proceedings Article | 20 March 2006 Paper

Intelligent model-based OPC

W.C. Huang, C.M. Lai, B. Luo, C.K. Tsai, M.H. Chih, C.W. Lai, C.C. Kuo, R.G. Liu, H.T. Lin

Proceedings Volume 6154, 615436 (2006) https://doi.org/10.1117/12.657792

KEYWORDS: Optical proximity correction, Lithography, Genetic algorithms, Model-based design, Optimization (mathematics), Artificial neural networks, Image segmentation, Neural networks, Semiconducting wafers, Systems modeling

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Optical proximity correction is the technique of pre-distorting mask layouts so that the printed patterns are as close to the desired shapes as possible. For model-based optical proximity correction, a lithographic model to predict the edge position (contour) of patterns on the wafer after lithographic processing is needed. Generally, segmentation of edges is performed prior to the correction. Pattern edges are dissected into several small segments with corresponding target points. During the correction, the edges are moved back and forth from the initial drawn position, assisted by the lithographic model, to finally settle on the proper positions. When the correction converges, the intensity predicted by the model in every target points hits the model-specific threshold value. Several iterations are required to achieve the convergence and the computation time increases with the increase of the required iterations. An artificial neural network is an information-processing paradigm inspired by biological nervous systems, such as how the brain processes information. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. A neural network can be a powerful data-modeling tool that is able to capture and represent complex input/output relationships. The network can accurately predict the behavior of a system via the learning procedure. A radial basis function network, a variant of artificial neural network, is an efficient function approximator. In this paper, a radial basis function network was used to build a mapping from the segment characteristics to the edge shift from the drawn position. This network can provide a good initial guess for each segment that OPC has carried out. The good initial guess reduces the required iterations. Consequently, cycle time can be shortened effectively. The optimization of the radial basis function network for this system was practiced by genetic algorithm, which is an artificially intelligent optimization method with a high probability to obtain global optimization. From preliminary results, the required iterations were reduced from 5 to 2 for a simple dumbbell-shape layout.

Proceedings Article | 12 May 2005 Paper

OPC modeling by genetic algorithm

W. Huang, C. Lai, B. Luo, C. Tsai, C. Tsay, C. Kuo, R. Liu, H. Lin, B. Lin

Proceedings Volume 5754, (2005) https://doi.org/10.1117/12.602096

KEYWORDS: Optical proximity correction, Lithography, Wafer-level optics, Genetic algorithms, Model-based design, Data modeling, Optimization (mathematics), Optical lithography, Photomasks, Semiconducting wafers

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Proceedings Article | 28 May 2004 Paper

Phenomena and OPC solution of ripple patterns for 65-nm node

Chih-Ming Lai, Jeng-Shiun Ho, Chien-Wen Lai, Cheng-Kun Tsai, Cherng-Shyan Tsay, Jeng-Horng Chen, Ru-Gun Liu, Yao Ku, Burn-Jeng Lin

Proceedings Volume 5377, (2004) https://doi.org/10.1117/12.544250

KEYWORDS: Optical proximity correction, Optical lithography, Algorithm development, Lithography, Photomasks, Critical dimension metrology

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