Fast lithographic source pupil optimization using difference of convex functions algorithm for transformed L1 penalty

Fast source pupil optimization (SO) has appeared as an important technique for improving lithographic imaging fidelity and process window (PW) in holistic lithography at 7-5nm node. Gradient-based methods are generally used in current SO. However, most of these methods are time-consuming. In our previous work, compressive sensing (CS) theory is applied to accelerate the SO procedure, where the SO is formulated as an underdetermined linear problem by randomly sampling monitoring pixels on mask features. CS-SO theory assumes that the source pattern is a sparse pattern on a certain basis, then the SO is transformed into a L1-norm or Lp-norm (0

Keywords:

• Convex function
• Algorithm
• Iterative reconstruction
• Basis (linear algebra)
• Relaxation (iterative method)
• Inverse problem
• Function (mathematics)
• Underdetermined system
• Compressed sensing
• Computer science

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