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 to in a normal fab because of a difficulty of insitu measuring. An empirical E0(dose-to-clear) swing curve is used to extract ABC exposure parameters previously by our group [1]. 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 ofABC 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 niuch 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 ofthe swing curve, to the neural network algorithm. As a result, Dill's ABC parameters were able to be rapidly and accurately extracted with some of the quantified values ofthe swing curve. This method ofextracting 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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Kruskal and Wallis (1952) proposed a nonparametric method to test the differences between more than three independent treatments. This procedure uses rank in mixed sample combined with more than three unlike populations. This paper proposes a the new procedure based on joint placements for a one-way layout as extension of the joint placements described in Chung and Kim (2007). A Monte Carlo simulation study is adapted to compare the power of the proposed method with previous methods.

P r o c e s s p r o x i m i t y c o r r e c t i o n b y using neural n e t w o r k s tHynix Semiconductor, San 136-1. Ami-ri. Oubal-sub. Kyoungki-do. 467-701. Korea a) Abstract Making an accurate and quick critical dimension (CD) prediction is required for higher integrated device. Because simulation tools are consisted of many process parameters and models. it is hard that process parameters are calibrated to match with the CD results for various patterns. This paper presents a method o f improving accuracy of predicting CD results by applying A (the difference between simulation and experimental data) value t o neural network algorithm ("A) t o reduce CD the difference caused by optical proximity effect.