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Sampling-based imaging model for fast source and mask optimization in immersion lithography.

Yiyu Sun, Yanqiu Li, Guanghui Liao

|February 24, 2022

Summary

This summary is machine-generated.

This study introduces a novel forward imaging model to accelerate source and mask optimization (SMO) for faster inverse lithography. The new model speeds up existing SMO algorithms by three times.

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Area of Science:

Computational lithography
Optical physics
Image processing

Background:

Current source and mask optimization (SMO) research primarily focuses on inverse optimization algorithms.
Innovations in forward imaging models are crucial for enhancing computational efficiency in SMO procedures, but receive limited attention.

Purpose of the Study:

To develop an advanced framework for fast inverse lithography by improving forward imaging models for source and mask optimization.
To investigate the impact of a novel sampling-based imaging model on the computational efficiency of SMO.

Main Methods:

Establishment of a sampling-based imaging model incorporating an inverse point spread function.
Reduction of computational dimensions through the innovative imaging model.
Simulation of the proposed SMO method utilizing the novel imaging model.

Main Results:

The proposed sampling-based imaging model significantly reduces computational dimensions.
The novel forward imaging model accelerates existing algorithm-accelerated SMO procedures.
Simulations demonstrate a threefold speedup in SMO procedures compared to current methods.

Conclusions:

Innovations in forward imaging models offer a more significant impact on computational efficiency for SMO than inverse optimization algorithms alone.
The developed sampling-based imaging model provides an advanced and efficient framework for fast inverse lithography.
This research highlights a promising direction for accelerating computational lithography processes.