Nonlinear compressive inverse lithography aided by low-rank regularization.

Area of Science:

• Semiconductor Manufacturing
• Computational Lithography
• Integrated Circuit Fabrication

Background:

• Photolithography is fundamental to semiconductor fabrication of microscale and nanoscale integrated circuits.
• Inverse Lithography Technique (ILT) corrects patterning distortions by pre-distorting mask patterns.
• Existing ILT methods often face substantial computational complexity challenges.

Purpose of the Study:

• To develop a computationally efficient and accurate Inverse Lithography Technique (ILT) framework.
• To improve image fidelity and control mask complexity in semiconductor patterning.
• To address the limitations of traditional ILT methods regarding computational cost.

Main Methods:

• A nonlinear compressive sensing framework was developed for ILT.
• The method utilizes a nonlinear lithography imaging model and formulates ILT as an inverse optimization problem.
• A downsampling strategy reduces cost function dimensionality, while sparsity and low-rank regularizations constrain the mask pattern. The split Bregman algorithm solves the optimization problem.

Main Results:

• The proposed nonlinear compressive sensing framework significantly improves computational efficiency.
• The method enhances image fidelity, ensuring the projected pattern closely matches the intended design.
• Mask complexity is effectively controlled, leading to more manufacturable photomasks.
• Simulations demonstrate the superiority of the proposed method over traditional ILT algorithms.

Conclusions:

• The developed nonlinear compressive sensing framework offers an efficient and effective solution for Inverse Lithography Technique.
• This approach advances semiconductor manufacturing by enabling higher fidelity patterning with reduced computational demands.
• The method provides a practical tool for optimizing mask design in the fabrication of advanced integrated circuits.