Application of Exposure Simulation System to Reduce Isolated-Dense Bias by Using Annular Off-Axis Illumination

碩士 === 元智大學 === 電機工程學系 === 95 === As the optical lithography is pushed into the smaller feature size below the exposure wavelength, the optical proximity effect (OPE) is one of the most serious problems. The way to solve this problem is to use optical proximity correction (OPC) or phase shift mask (...

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Bibliographic Details
Main Authors: Yi-Nan Shih, 石宜男
Other Authors: Nien-Po Chen
Format: Others
Language:zh-TW
Published: 2007
Online Access:http://ndltd.ncl.edu.tw/handle/89228645650663503131
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Summary:碩士 === 元智大學 === 電機工程學系 === 95 === As the optical lithography is pushed into the smaller feature size below the exposure wavelength, the optical proximity effect (OPE) is one of the most serious problems. The way to solve this problem is to use optical proximity correction (OPC) or phase shift mask (PSM). However, the OPC technique crucially depends on the huge amount of mask data, and it is difficult to make the PSM with zero defects. Although many OPC methods have been proposed to correct critical dimension (CD) errors, mask makers have the difficulty in the mask fabrication and inspection. Therefore, it is preferable to reduce the CD variation without complicated correction methods, if possible. Isolated-Dense bias (IDB) is becoming a critical parameter in the evaluation of 0.13 μm process performance. In order to improve the resolution and the depth of focus (DOF), we investigated the annular off-axis illumination (OAI) method. Optimization of OAI conditions to reduce the IDB is essential. The methods of illumination, the numerical aperture (NA) of the projection lens, the degree of coherence (Sigma, σ) could change IDB. Finally, on the basis of the simulation analysis of the illumination method, the optimum optical parameters are selected to obtain the high resolution and enough DOF to reduce IDB value. Facing with many parameters in the lithography processes, the optimization is performed with the aid of Taguchi method. The method allows us to efficiently study the interaction among the parameters and obtain the optimal values.