Machine Learning based Optical Proximity Correction Techniques

The shrinking of the size of the advanced technological nodes brings up new challenges to the semiconductor manufacturing community.The optical proximity correction(OPC)is invented to reduce the errors of the lithographic process.The conventional OPC techniques rely on the empirical models and optimization methods of iterative type.Both the accuracy and computing speed of the existing OPC techniques need to be improved to fulfill the stringent requirement of the research and design for latest technological nodes.The emergence of machine learning technologies inspires novel OPC algorithms.More accurate forward simulation of the lithographic process and single turn optimization methods are enabled by the machine learning based OPC techniques.We discuss the latest progress made by the OPC community in the process simulation and optimization based on machine learning techniques.

A sparse matrix model-based optical proximity correction algorithm with model-based mapping between segments and control sites

Optical proximity correction (OPC) is a key step in modern integrated circuit (IC) manufacturing.The quality of model-based OPC (MB-OPC) is directly determined by segment offsets after OPC processing.However,in conventional MB-OPC,the intensity of a control site is adjusted only by the movement of its corresponding segment;this scheme is no longer accurate enough as the lithography process advances.On the other hand,matrix MB-OPC is too time-consuming to become practical.In this paper,we propose a new sparse matrix MB-OPC algorithm with model-based mapping between segments and control sites.We put forward the concept of 'sensitive area'.When the Jacobian matrix used in the matrix MB-OPC is evaluated,only the elements that correspond to the segments in the sensitive area of every control site need to be calculated,while the others can be set to 0.The new algorithm can effectively improve the sparsity of the Jacobian matrix,and hence reduce the computations.Both theoretical analysis and experiments show that the sparse matrix MB-OPC with model-based mapping is more accurate than conventional MB-OPC,and much faster than matrix MB-OPC while maintaining high accuracy.

Study of Inverse Lithography Approaches based on Deep Learning

Computational lithography(CL)has become an indispensable technology to improve imaging resolution and fidelity of deep sub-wavelength lithography.The state-of-the-art CL approaches are capable of optimizing pixel-based mask patterns to effectively improve the degrees of optimization freedom.However,as the growth of data volume of photomask layouts,computational complexity has become a challenging problem that prohibits the applications of advanced CL algorithms.In the past,a number of innovative methods have been developed to improve the computational efficiency of CL algorithms,such as machine learning and deep learning methods.Based on the brief introduction of optical lithography,this paper reviews some recent advances of fast CL approaches based on deep learning.At the end,this paper briefly discusses some potential developments in future work.

SVM based layout retargeting for fast and regularized inverse lithography

Inverse lithography technology(ILT),also known as pixel-based optical proximity correction(PB-OPC),has shown promising capability in pushing the current 193 nm lithography to its limit.By treating the mask optimization process as an inverse problem in lithography,ILT provides a more complete exploration of the solution space and better pattern fidelity than the traditional edge-based OPC.However,the existing methods of ILT are extremely time-consuming due to the slow convergence of the optimization process.To address this issue,in this paper we propose a support vector machine(SVM)based layout retargeting method for ILT,which is designed to generate a good initial input mask for the optimization process and promote the convergence speed.Supervised by optimized masks of training layouts generated by conventional ILT,SVM models are learned and used to predict the initial pixel values in the‘undefined areas’of the new layout.By this process,an initial input mask close to the final optimized mask of the new layout is generated,which reduces iterations needed in the following optimization process.Manufacturability is another critical issue in ILT;however,the mask generated by our layout retargeting method is quite irregular due to the prediction inaccuracy of the SVM models.To compensate for this drawback,a spatial filter is employed to regularize the retargeted mask for complexity reduction.We implemented our layout retargeting method with a regularized level-set based ILT(LSB-ILT)algorithm under partially coherent illumination conditions.Experimental results show that with an initial input mask generated by our layout retargeting method,the number of iterations needed in the optimization process and runtime of the whole process in ILT are reduced by 70.8%and 69.0%,respectively.

A novel OPC method to reduce mask volume with yield-aware dissection

Growing data volume of masks tremendously increases manufacture cost. The cost increase is partially due to the complicated optical proximity corrections applied on mask design. In this paper, a yield-aware dissec- tion method is presented. Based on the recognition of yield related mask context, the dissection result provides sufficient degrees of freedom to keep fidelity on critical sites while still retaining the frugality of modified designs. Experiments show that the final mask volume using the new method is reduced to about 50% of the conventional method.