Source and mask joint optimization(SMO)is a widely used computational lithography method for state-of-the-art optical lithography process to improve the yield of semiconductor wafers.Nowadays,computational efficiency ...Source and mask joint optimization(SMO)is a widely used computational lithography method for state-of-the-art optical lithography process to improve the yield of semiconductor wafers.Nowadays,computational efficiency has become one of the most challenging issues for the development of pixelated SMO techniques.Recently,compressive sensing(CS)theory has be explored in the area of computational inverse problems.This paper proposes a CS approach to improve the computational efficiency of pixel-based SMO algorithms.To our best knowledge,this paper is the first to develop fast SMO algorithms based on the CS framework.The SMO workflow can be separated into two stages,i.e.,source optimization(SO)and mask optimization(MO).The SO and MO are formulated as the linear CS and nonlinear CS reconstruction problems,respectively.Based on the sparsity representation of the source and mask patterns on the predefined bases,the SO and MO procedures are implemented by sparse image reconstruction algorithms.A set of simulations are presented to verify the proposed CS-SMO methods.The proposed CS-SMO algorithms are shown to outperform the traditional gradient-based SMO algorithm in terms of both computational efficiency and lithography imaging performance.展开更多
Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques....Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques. But the randomicity of parameter selection in its implement often prevents it achieving expected performance. By utilizing genetic algorithm (GA) to optimize the parameters in data preprocessing and the training model of SVM simultaneously, a hybrid optimization algorithm is proposed in the paper to address this problem. The experimental results demonstrate that it’s an effective method and can improve the performance of SVM-based intrusion detection system further.展开更多
基金the National Natural Science Foundation of China(NSFC)(61675021)the Fundamental Research Funds for the Central Universities(2018CX01025).
文摘Source and mask joint optimization(SMO)is a widely used computational lithography method for state-of-the-art optical lithography process to improve the yield of semiconductor wafers.Nowadays,computational efficiency has become one of the most challenging issues for the development of pixelated SMO techniques.Recently,compressive sensing(CS)theory has be explored in the area of computational inverse problems.This paper proposes a CS approach to improve the computational efficiency of pixel-based SMO algorithms.To our best knowledge,this paper is the first to develop fast SMO algorithms based on the CS framework.The SMO workflow can be separated into two stages,i.e.,source optimization(SO)and mask optimization(MO).The SO and MO are formulated as the linear CS and nonlinear CS reconstruction problems,respectively.Based on the sparsity representation of the source and mask patterns on the predefined bases,the SO and MO procedures are implemented by sparse image reconstruction algorithms.A set of simulations are presented to verify the proposed CS-SMO methods.The proposed CS-SMO algorithms are shown to outperform the traditional gradient-based SMO algorithm in terms of both computational efficiency and lithography imaging performance.
基金This work was supported by the Research Grant of SEC E-Institute :Shanghai High Institution Grid and the Science Foundation ofShanghai Municipal Commission of Science and Technology No.00JC14052
文摘Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques. But the randomicity of parameter selection in its implement often prevents it achieving expected performance. By utilizing genetic algorithm (GA) to optimize the parameters in data preprocessing and the training model of SVM simultaneously, a hybrid optimization algorithm is proposed in the paper to address this problem. The experimental results demonstrate that it’s an effective method and can improve the performance of SVM-based intrusion detection system further.
