Papers

10

Total Citations

450

H-Index

8

About

Shengwei Ding is a prominent researcher specializing in scheduling optimization and throughput analysis of semiconductor manufacturing equipment, particularly cluster tools. His work has made foundational contributions to one of the most complex challenges in chip fabrication: efficiently coordinating robotic wafer-handling systems within multicluster tool configurations. Ding's most influential contributions include a two-part series on optimal scheduling of multicluster tools with constant robot moving times (2010), which together have garnered over 200 citations and established rigorous algorithmic frameworks for both serial and tree-like topology configurations. His 2008 paper on steady-state throughput analysis using a decomposition approach (131 citations) provided a landmark methodology for breaking down complex multicluster scheduling problems into tractable components, fundamentally advancing how researchers and engineers model these systems. Earlier work, including his 2005 and 2007 papers, laid the groundwork for understanding linear cluster tools and multi-unit production cycles, helping to bridge single-cluster theory with more complex, real-world equipment configurations. His development of event graph-based simulation methods further enriched the toolkit available to scheduling researchers. With a cumulative citation count exceeding 450, Ding's body of work remains an essential reference for engineers and academics working at the intersection of operations research and semiconductor manufacturing.

Research Focus

Key Achievements

8
H-Index
10
Papers
450
Total Citations
45
Avg Citations/Paper
🏆 Most Cited Paper
Optimal Scheduling of Multicluster Tools With Constant Robot Moving Times, Part I: Two-Cluster Analysis
133 citations · 2010
📈 Most Prolific Year: 2007 (3 Papers)
🤝 Key Collaborators: 9
🏛 Institutions: Nestlé (United States), University of California, Berkeley, Rensselaer Polytechnic Institute

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
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