Binary mask optimization for inverse lithography with partially coherent illumination

Area of Science:

• Photolithography
• Computational lithography
• Semiconductor manufacturing

Background:

• Gradient-based optimization methods have advanced inverse lithography under coherent illumination.
• Practical photolithography systems commonly use partially coherent illumination, posing unique challenges.
• Existing methods often struggle with the nonlinearities inherent in partially coherent systems.

Purpose of the Study:

• To develop gradient-based binary mask optimization methods for partially coherent lithography.
• To address and model the nonlinearities present in partially coherent illumination systems.
• To enhance manufacturability of lithographic patterns through advanced optimization.

Main Methods:

• Utilizing two nonlinear models for optimization: a Fourier representation and an average coherent approximation.
• Implementing gradient-based optimization for binary mask design.
• Incorporating wavelet regularization to improve pattern manufacturability.

Main Results:

• Successful development of gradient-based optimization techniques tailored for partially coherent systems.
• Demonstration of two distinct nonlinear models for accurate system representation.
• Integration of wavelet regularization to guide solutions towards manufacturable patterns.

Conclusions:

• The developed methods offer effective solutions for inverse lithography under partially coherent illumination.
• The nonlinear models and wavelet regularization contribute to improved mask optimization and pattern quality.
• This work advances the capabilities of computational lithography for next-generation semiconductor devices.