Fast model-based optical proximity correction method for plasmonic lithography

Area of Science:

• Nanotechnology
• Optical Engineering
• Materials Science

Background:

• Plasmonic lithography offers sub-diffraction limit resolution but suffers from optical proximity effects, hindering perfect pattern replication.
• Existing methods require optimization to mitigate pattern errors and enhance imaging fidelity.

Purpose of the Study:

• To propose and validate a fast, model-based optical proximity correction (OPC) method specifically for plasmonic lithography.
• To improve pattern accuracy and imaging fidelity in plasmonic lithography through mask pattern optimization.

Main Methods:

• Developed a novel OPC method based on an imaging model derived from improved rigorous coupled wave analysis (RCWA).
• Employed an edge-movement strategy within a feedback closed-loop system for iterative mask pattern optimization.
• Utilized edge placement error (EPE) from previous iterations to guide edge fragment movement in subsequent steps.

Main Results:

• Achieved convergence in approximately seven iterations, demonstrating rapid OPC process.
• Reported an average reduction of 76% in the sum of absolute EPE (|EPE|) and 71% in pattern error (PE).
• Demonstrated good compatibility across patterns with varying line width sizes.

Conclusions:

• The proposed model-based OPC method is efficient and effective for plasmonic lithography.
• This technique significantly enhances imaging fidelity and reduces pattern errors.
• The OPC method shows promise for practical application in advanced lithography.