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Multi-objective and multi-solution source mask optimization using NSGA-II for more direct process window enhancement.

Qingyan Zhang, Liu Junbo, Haifeng Sun

|March 5, 2024

Summary

This summary is machine-generated.

This study introduces NSGA-SMO, a new method for source and mask optimization (SMO) in photolithography. It enhances process window (PW) performance, improving lithography robustness and imaging quality for advanced critical dimensions (CD).

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Area of Science:

Semiconductor Manufacturing
Optical Engineering
Computational Lithography

Background:

Source and Mask Optimization (SMO) is crucial for photolithography resolution enhancement as critical dimensions (CD) shrink.
Traditional SMO methods focus on in-focus imaging quality, neglecting the Process Window (PW), which includes Depth of Focus (DOF) and Exposure Latitude (EL), vital for lithography robustness.
Evaluating PW is computationally intensive and challenging for gradient-based SMO algorithms.

Purpose of the Study:

To develop a novel SMO method that directly optimizes Process Window (PW) performance.
To enhance the robustness of SMO results in advanced technology nodes by considering lithographic process variations.
To maintain high in-focus image quality while improving lithographic process margins.

Main Methods:

Proposed a novel Process Window enhancement SMO method, termed NSGA-SMO, utilizing the Nondominated Sorting Genetic Algorithm II (NSGA-II).
Employed the Variational Lithography Model (VLIM), a fast focus-variation aerial image model, for direct PW optimization.
Implemented a multi-objective optimization approach to balance in-focus imaging quality and PW performance.

Main Results:

NSGA-SMO demonstrated significant improvements in Depth of Focus (DOF) and Exposure Latitude (EL) compared to conventional multi-objective SMO.
Simulations showed over 20% improvement in DOF and EL for typical patterns.
For complicated patterns, NSGA-SMO achieved results up to four times superior to single-objective SMO.

Conclusions:

The proposed NSGA-SMO method effectively optimizes Process Window (PW) performance in photolithography.
This approach enhances lithographic process robustness, crucial for advanced critical dimensions (CD).
NSGA-SMO offers a viable solution for improving imaging quality and process margins in high-volume manufacturing.