AI Mask Image Generation for Semiconductor Mask Rule Compliance
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Solution Overview
Problem
Existing optical proximity correction (OPC) methods for generating semiconductor masks rely heavily on user expertise, limiting performance and speed in determining optimal rules for mask fabrication.
Innovation Solution
Employing artificial intelligence (AI) models to generate mask images by training with target patterns, applying loss functions to correct mask images based on diffraction considerations, and updating the AI models to meet mask rules, including operations like opening and closing to adjust pattern size and spacing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional OPC methods are used to generate mask images, then mask fabrication rules can be met, but the performance and speed are limited by user expertise in choosing optimal rules
Solution Approach 1:
The AI model automatically learns and applies optimal mask correction rules through training on target patterns and corrected mask images, eliminating the need for manual rule selection by users. The system self-optimizes by minimizing loss functions that measure the difference between generated and corrected masks, thereby achieving both rule compliance and high-speed generation without human intervention.
2Productivity
If AI models are used to generate mask images automatically, then generation speed improves, but the initial model performance may not meet mask fabrication rules
Solution Approach 1:
The system implements a feedback mechanism where the generated mask images are compared against corrected reference masks using loss functions. The AI model receives feedback through gradient-based optimization, adjusting its internal parameters to minimize the difference between generated and corrected masks. This iterative feedback loop ensures the model learns to produce rule-compliant masks while maintaining high generation speed.
Solution Approach 2:
The system performs preliminary training actions by pre-processing target patterns and preparing corrected mask images as training data before actual mask generation. The AI model is pre-trained on diverse pattern data to learn fundamental correction rules, enabling it to quickly adapt to specific fabrication requirements while ensuring initial rule compliance.
3Manufacturing precision
If multiple corrections are applied to mask images to meet fabrication rules, then manufacturing precision improves, but the complexity of the generation process increases
Solution Approach 1:
The system merges multiple correction operations into a single integrated AI model. Instead of applying separate correction steps for different fabrication rules, the model learns to simultaneously satisfy multiple constraints (minimum pattern size, minimum spacing, etc.) through unified training. This consolidation reduces process complexity while maintaining high manufacturing precision by learning the interrelationships between different correction requirements.
Data Source
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AI summary
A method for generating a mask image for fabricating a mask includes: generating the mask image from a target pattern using a first artificial intelligence (AI) model; calculating a first loss function based on a difference between the mask image and a mask image corrected according to a mask rule; and updating the first AI model based on the first loss function.