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Patterning via Optical Saturable Transitions - Fabrication and Characterization

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Binary mask optimization for inverse lithography with partially coherent illumination.

Xu Ma¹, Gonzalo Arce

¹Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware 19716, USA. maxu@udel.edu

Journal of the Optical Society of America. A, Optics, Image Science, and Vision

|November 28, 2008

Summary

This study introduces new gradient-based methods for optimizing binary masks in partially coherent lithography. These methods address system nonlinearities, improving pattern manufacturability for advanced semiconductor fabrication.

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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.