Multimodal Reasoning for Semiconductor Fault Root-Cause Analysis

The patent replaces manual feature engineering (mechanical/process-based approach) with deep learning models that automatically extract features from raw data. The neural networks directly process unstructured sensor signals, images, and time-series data without requiring manual intervention to create feature representations, thereby substituting the mechanical feature engineering process with automated intelligent processing.

The system enables self-service by allowing the machine learning models to automatically learn and extract relevant features from raw data patterns themselves. The models perform self-training and self-optimization through continuous learning from new data, eliminating the need for external manual feature engineering and enabling the system to adapt autonomously to new error patterns.

The patent transforms static knowledge graphs into dynamic, self-evolving structures. The knowledge graphs continuously update themselves by incorporating new error patterns, failure mechanisms, and causal relationships discovered through ongoing data analysis. This dynamic adaptation allows the system to evolve its knowledge base automatically without manual re-curation, maintaining both structure and adaptability.

The system implements feedback loops where diagnostic outcomes and new error data continuously feed back into the knowledge graph. This feedback mechanism enables the knowledge graph to learn from actual operational data, refine its causal relationships, and adapt to emerging failure modes, thereby maintaining high-quality knowledge representation while gaining adaptability to new patterns.

The patent segments the complex diagnostic task into multiple specialized components: deep learning models for feature extraction, vector databases for pattern matching, knowledge graphs for causal reasoning, and multi-hop reasoning engines for traceability. This segmentation allows each component to handle specific aspects of the diagnostic process efficiently, reducing overall system complexity while enabling comprehensive automated root-cause analysis.

The patent introduces intermediate structures such as vector representations and embedded knowledge graphs that act as mediators between raw data and final diagnoses. These intermediaries bridge the gap between different data types and reasoning processes, enabling automated tracing of causal chains without requiring complex direct correlations, thereby reducing both time and computational complexity.

The patent creates a composite diagnostic system that integrates multiple specialized components: deep learning models for pattern recognition, vector databases for similarity search, knowledge graphs for causal relationships, and reasoning engines for multi-hop inference. This composite architecture combines the strengths of different approaches, enabling both comprehensive data processing and sophisticated domain-specific reasoning that neither component could achieve alone.

The patent adds new dimensions to the reasoning process by introducing multi-hop causal reasoning capabilities that traverse multiple levels of the knowledge graph. This dimensional expansion allows the system to move beyond simple pattern matching into deep causal analysis, tracing errors through multiple intermediate causes and effects to identify root causes in complex manufacturing processes.