DeCo: Defect-Aware Modeling with Contrasting Matching for Optimizing Task Assignment in Online IC Testing
Lo Pang-Yun Ting, Yu-Hao Chiang, Yi-Tung Tsai, Hsu-Chao Lai, Kun-Ta Chuang·May 01, 2025
Summary
DeCo, an AI framework, optimizes task assignment in online IC testing by constructing a defect-aware graph, enhancing differentiation with limited data. It balances workloads, successfully assigning unfamiliar tasks to capable engineers, demonstrating AI's potential in real-world IC failure analysis.
Introduction
Background
Overview of the IC testing industry
Challenges in task assignment and differentiation with limited data
Objective
To present DeCo, an AI framework that optimizes task assignment in online IC testing
Highlighting how DeCo constructs a defect-aware graph to enhance differentiation
Discussing the framework's ability to balance workloads and assign unfamiliar tasks to capable engineers
Method
Data Collection
Sources of data for the defect-aware graph
Types of data collected (e.g., historical test results, engineer performance metrics)
Data Preprocessing
Techniques used to clean and prepare the data for analysis
Methods for handling missing or incomplete data
Graph Construction
Process of creating a defect-aware graph
Key features and attributes of the graph nodes and edges
AI Algorithm
Description of the AI algorithm used for task assignment
How the algorithm leverages the defect-aware graph for optimization
Workload Balancing
Strategies for balancing workloads among engineers
Implementation of mechanisms to ensure fair and efficient task distribution
Task Assignment
Criteria for assigning unfamiliar tasks to capable engineers
Case studies demonstrating successful task assignments
Results
Performance Evaluation
Metrics used to assess DeCo's effectiveness
Comparison with traditional task assignment methods
Case Studies
Detailed examples of DeCo's application in real-world IC failure analysis
Outcomes and benefits observed in terms of task completion, engineer productivity, and workload management
Conclusion
Summary of DeCo's Contributions
Recap of DeCo's role in enhancing task differentiation and workload balancing
Future Directions
Potential improvements and extensions of DeCo
Research opportunities in AI for IC testing and beyond
Implications for Industry
Impact of AI-driven task assignment on the IC testing industry
Recommendations for adopting AI frameworks like DeCo in real-world scenarios
Basic info
papers
artificial intelligence
Advanced features
Insights
What are the key steps involved in optimizing task assignment using DeCo in IC testing?
In what ways does DeCo enhance differentiation with limited data in IC failure analysis?
How does the DeCo framework construct a defect-aware graph for task assignment?
How does DeCo balance workloads and assign unfamiliar tasks to capable engineers?