Photomask Defect Correction via Difficulty-Based Candidate Selection
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Solution Overview
Problem
The complexity of designing and manufacturing photomasks with sub-resolution assist features leads to lengthy defect correction times and low success rates in semiconductor device manufacturing, as existing methods struggle to efficiently correct pattern defects in fine patterns.
Innovation Solution
A correction pattern generation device and system that includes a processor to detect pattern defects, generate correction pattern candidates, calculate correction difficulty, and select optimal correction patterns based on calculated difficulty and exposure likelihood, facilitating efficient defect correction on photomasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If sub-resolution assist features are included in photomask patterns to form sub-resolution limit features, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The invention segments the defect correction process into multiple candidate patterns, each addressing different aspects of the defect. The correction pattern generation device creates multiple distinct correction candidates (e.g., different auxiliary pattern configurations) and evaluates them separately, allowing complex defect correction to be broken down into manageable segments that can be independently assessed and combined.
Solution Approach 2:
The invention introduces dynamic selection of correction patterns based on calculated correction difficulty degrees. Rather than using a fixed correction approach, the system dynamically evaluates multiple candidates and selects the optimal correction pattern for each specific defect situation, adapting the correction strategy to the unique characteristics of each defect and pattern configuration.
2Manufacturing precision
If complicated patterns with auxiliary features are used, then manufacturing precision is improved, but correction time increases when defects occur
Solution Approach 1:
The invention performs preliminary generation of multiple correction pattern candidates before actual defect correction is needed. By pre-calculating and storing multiple correction options with their associated difficulty degrees, the system prepares correction strategies in advance, so that when a defect occurs, the correction can be quickly selected and applied without time-consuming analysis during the correction process itself.
Solution Approach 2:
The invention replaces manual or trial-and-error defect correction methods with an automated computational system that calculates correction difficulty degrees and selects optimal patterns algorithmically. This substitution of mechanical/manual correction processes with automated computational evaluation dramatically reduces correction time while maintaining high precision for complicated patterns with auxiliary features.
3Reliability
If multiple correction pattern candidates are generated and evaluated, then correction success rate is improved, but device complexity increases
Solution Approach 1:
The correction pattern generation device performs self-evaluation of multiple correction candidates by automatically calculating correction difficulty degrees for each candidate. The system serves itself by autonomously assessing its own correction options, selecting the most appropriate pattern without requiring external manual evaluation, thereby improving success rates while managing complexity through automated self-assessment.
Solution Approach 2:
The invention implements a feedback mechanism where correction patterns are evaluated based on calculated difficulty degrees, and this evaluation feedback is used to select the optimal correction candidate. The system uses the calculated metrics as feedback to guide the selection process, creating a closed-loop system that continuously improves correction success rates by learning from the evaluation results of multiple candidates.
Data Source
AI summary
A correction pattern generation device includes a processor configured to receive pattern information for a mask including a defect in a pattern formed on the mask, generate a correction pattern candidate for correcting the defect, calculate a correction difficulty degree for the correction pattern candidate, and select a correction pattern from correction pattern candidates based on the calculated correction difficulty degree for each correction pattern candidate if more than one correction pattern candidate for correcting the defect is generated.


