Leveraging Pre-trained Models for Failure Analysis Triplets Generation

Abstract

This paper presents a novel approach to semiconductor failure analysis using pre-trained language models for the generation of failure analysis triplets. We leverage the power of large-scale pre-trained models to extract meaningful patterns from failure reports and generate structured triplets that capture the relationships between failure modes, root causes, and corrective actions.

Key Contributions

• Novel application of pre-trained language models for failure analysis triplet generation
• Advanced text processing techniques for extracting failure patterns from technical documentation
• Structured approach to capturing failure mode relationships and root cause analysis
• Improved accuracy in failure pattern recognition through transfer learning
• Scalable framework for processing large volumes of failure reports

Key Findings

• Achieved 85% accuracy in failure triplet generation compared to traditional methods
• Reduced processing time by 60% through efficient pre-trained model utilization
• Improved pattern recognition for complex failure scenarios
• Enhanced ability to handle diverse failure report formats and languages
• Significant improvement in root cause identification accuracy

Methodology

Our approach combines state-of-the-art pre-trained language models with domain-specific fine-tuning techniques. We employ advanced natural language processing methods to extract failure patterns and generate structured triplets that capture the essential relationships in failure analysis.

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