12 June 2003 Extraction of exposure parameters by using neural networks
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Abstract
Dill’s ABC parameters are key parameters for the simulation of photolithography patterning. The exposure parameters of each resist should be exactly known to simulate the desired pattern. In ordinary extracting methods of Dill’s ABC parameters, the changed refractive index and the absorption coefficient of photoresist are needed during exposure process. Generally, these methods are not easy to be applied in a normal fab because of a difficulty of in-situ measuring. An empirical E0 (dose-to-clear) swing curve is used to extract ABC exposure parameters previously by our group. Dill’s ABC parameters are not independent from each other and different values of them would cause the dose to clear swing curve variation. By using the known relationship of ABC parameters, the experimental swing curves are to be matched with the simulated ones in order to extract the parameters. But sometimes this method is not easy in matching the procedure and performing simulation. This procedure would take much time for matching between the experimental data and the simulation by the naked eyes, and also the simulations are performed over and over again for different conditions. In this paper, Dill’s ABC parameters were extracted by applying the values, which are quantitatively determined by measuring the mean value, period, slope, and amplitude of the swing curve, to the neural network algorithm. As a result, Dill’s ABC parameters were able to rapidly and accurately extracted with some of the quantified values of the swing curve. This method of extracting the exposure parameters can be used in a normal fab so that any engineer can easily obtain the exposure parameters and apply them to the simulation tools.
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Kyoung-Ah Jeon, Hyoung-Hee Kim, Ji-Yong Yoo, Jun-Taek Park, Hye-Keun Oh, "Extraction of exposure parameters by using neural networks", Proc. SPIE 5039, Advances in Resist Technology and Processing XX, (12 June 2003); doi: 10.1117/12.485050; https://doi.org/10.1117/12.485050
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