Genghong Wang

About

Genghong Wang is a leading researcher in semiconductor manufacturing automation, specializing in the scheduling and optimization of multi-cluster tools—the robotic systems at the heart of integrated-circuit fabrication. Their work addresses critical challenges in high-mix production, where multiple wafer types must be processed concurrently, and in system resilience under equipment failure. Wang’s most-cited paper (2022, 27 citations) pioneers optimal scheduling for dual-arm multi-cluster tools handling two wafer types, a breakthrough for high-mix chip production that existing studies had failed to solve. A second influential work (2023, 11 citations) tackles the complex problem of scheduling a single-arm two-cluster tool when a process module fails, ensuring wafer residency time constraints are met while maximizing throughput during closedown procedures. These contributions directly impact the efficiency and reliability of semiconductor fabs, reducing downtime and enabling flexible manufacturing. Wang’s research is essential reading for engineers and scholars in operations research, industrial engineering, and semiconductor equipment design, offering practical solutions to real-world production bottlenecks.

Research Focus

Key Achievements

Top Papers

1. 1
   Optimally Scheduling Dual-Arm Multi-Cluster Tools to Process Two Wafer Types

   27 citations · 2022
2. 2
   Scheduling a Single-Arm Two-Cluster Tool With a Process Module Failure Subject to Wafer Residency Time Constraints

   11 citations · 2023

Key Collaborators

Contact & Links