Learning-based compressive sensing method for EUV lithographic source optimization

Extreme ultraviolet (EUV) lithography emerges as a promising technique to fabricate next-generation integrated circuits. In order to improve the lithography imaging fidelity, source optimization (SO) technique is widely used to compensate for the imaging distortion. This paper develops an efficient learning-based SO approach for EUV lithography under the compressive sensing (CS) framework. The dimensionality of EUV-SO problem is significantly reduced by sparsely sampling the layout pattern. Then, the EUV-SO is formulated as an l₁-norm inverse reconstruction problem based on the sparse prior of source patterns. The cost function is established based on a rigorous imaging model to take into account the characteristic effects in EUV lithography systems. In addition, a learning-based method is proposed to jointly optimize the source dictionary and projection matrix according to the sparsity and incoherence conditions in CS theory. The optimal source dictionary and projection matrix can be learned from a set of training samples collected from typical layout features in advance. Then, the optimized dictionary and projection matrix can be repetitively used in the following SO algorithms. Based on a set of simulations, the proposed SO method is proved to achieve good performance in both imaging fidelity and computational efficiency.