Process proximity correction by neural networks

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 optimized to match with the CD results for various patterns. This paper presents a method of improving accuracy of predicting CD results by applying the CD difference between simulation and experimental data value to neural network algorithm to reduce the CD difference caused by optical proximity effect.