Big GCVAE: Decision-making with Adaptive Transformer

Abstract

This paper presents Big GCVAE, a novel decision-making framework that leverages adaptive transformers for semiconductor failure analysis. Our approach combines the power of generative models with adaptive attention mechanisms to achieve unprecedented accuracy in failure pattern recognition and root cause analysis.

Key Contributions

• Adaptive Transformer Architecture Novel transformer design that dynamically adjusts attention patterns based on failure analysis context and complexity.
• Generative Causal Modeling Integration of causal inference with generative models for improved interpretability and accuracy in failure analysis.
• Multi-Modal Learning Advanced framework for processing diverse data types including images, text, and numerical data in unified failure analysis pipeline.
• Real-time Adaptation System capable of learning and adapting to new failure patterns in real-time, improving accuracy over time.

Key Findings

• 91.7% Accuracy Achieved in failure analysis compared to 82.1% for human experts
• 4.2 Minutes Average diagnosis time vs 2.3 hours for manual analysis
• 60% Cost Reduction In computational resources while maintaining performance
• Real-time Learning Continuous improvement through adaptive learning mechanisms