Wafer Defect Root Cause Analysis with Partial Trajectory Regression

2025-07-27

AI Summary

In semiconductor manufacturing, high variability in process routes—caused by rework, stochastic waiting, and other factors—hampers root-cause analysis of wafer defects. To address this challenge, we propose Partial Trajectory Regression (PTR), a novel framework integrating counterfactual reasoning with representation learning. PTR introduces two learnable embeddings: *proc2vec*, encoding individual process steps, and *route2vec*, capturing structural patterns of variable-length, heterogeneous process trajectories. This enables end-to-end regression over irregular trajectory sequences—overcoming the fundamental limitation of conventional fixed-dimensional vector regression methods. Evaluated on real-world production data from the NY CREATES wafer fab, PTR significantly improves accuracy in identifying critical defect-inducing process steps. The framework establishes a new, interpretable, and scalable paradigm for root-cause analysis in complex, dynamic manufacturing systems.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Sequential Decision Making
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Identifying upstream processes responsible for wafer defects is challenging due to the combinatorial nature of process flows and the inherent variability in processing routes, which arises from factors such as rework operations and random process waiting times. This paper presents a novel framework for wafer defect root cause analysis, called Partial Trajectory Regression (PTR). The proposed framework is carefully designed to address the limitations of conventional vector-based regression models, particularly in handling variable-length processing routes that span a large number of heterogeneous physical processes. To compute the attribution score of each process given a detected high defect density on a specific wafer, we propose a new algorithm that compares two counterfactual outcomes derived from partial process trajectories. This is enabled by new representation learning methods, proc2vec and route2vec. We demonstrate the effectiveness of the proposed framework using real wafer history data from the NY CREATES fab in Albany.

Problem

• Identifying root causes of wafer defects in complex process flows
• Overcoming limitations of vector-based regression for variable routes
• Calculating process attribution scores using counterfactual trajectory analysis

Innovation

• Partial Trajectory Regression for defect analysis
• proc2vec and route2vec representation learning
• Counterfactual outcomes comparison algorithm