Computational lithography has been playing a critical role in enabling the semiconductor industry. After source mask co-optimization (SMO), inverse lithography has become the ultimate frontier of computational lithography. Full chip implementation of rigorous inverse lithography remains impractical because of enormous computational hardware resource requirements and long computational time, the situation exacerbates for EUV computational lithography where mask 3D effect is more pronounced. One very promising technique to overcome the barrier is to take full advantage of the maturing machine learning techniques based on neural network architecture. Some success has been achieved using deep convolution neural network (DCNN) to obtain inverse lithography technology (ILT) solution with significantly less computational time. In DCNN, to extract features with sufficient resolution and nearly complete representation, the feature extract layers are very complicated and lack of physical meaning. More importantly, the training requires large number of well balanced samples, which makes the training more difficult and time consuming. To alleviate the difficulties relating to DCNN, we have proposed the physics based optimal feature vector design for machine learning based computational lithography. The innovative physics based feature vector design eliminates the need of feature extraction layers in neural network, only layers for mapping function construction are needed, which greatly reduces the NN training time and accelerates the NN model SRAF generation for full chip. In this paper, we will present our machine learning based inverse lithography results with adaptive and dynamical sampling scheme for neural network training.
With semiconductor technology progressing beyond 5nm node, there is tremendous pressure on computational lithography to achieve both accuracy and speed. One very promising technique to accomplish this mission is to take full advantage of the maturing machine learning techniques based on neural network architecture. Some success has been achieved using convolution neural network (CNN) to obtain inverse lithography technology (ILT) solution with significantly less computational time. In general, CNN architecture consists of feature extraction layers and nonlinear mapping function construction layers. To train a CNN model requires a large amount of data and computational resource. To maintain certain intrinsic symmetries of imaging behavior, the feature extraction layers must be carefully engineered using weight sharing techniques or using well balanced training samples of different orientations, otherwise, feature extraction part will be skewed. It is therefore very desired to have a scheme that can obtain optimal feature vector for machine learning based computational lithography automatically without the need of feature extraction layers in CNN. In this paper, we will make an attempt to describe such a scheme and present our test results on machine learning based OPC and ILT solution. It should be understood that machine learning based computational lithography solutions do not possess the capability to replace conventional OPC or ILT completely due to its lack of required accuracy. However, it can provide an initial solution that is close enough to final OPC solution or ILT solution, therefore fast OPC and fast ILT can be realized.