We are seeking highly motivated researchers to join our project on “Planning and Scheduling of Automated Material Handling Systems in Semiconductor Manufacturing Environments.” This project is carried out in collaboration with leading semiconductor companies and targets real-world, industrially relevant challenges. Our goal is to advance cutting-edge research while ensuring strong practical applicability.
The research will concentrate on three interrelated areas:a) Semiconductor Equipment Scheduling: In semiconductor fabs, a vast number of wafers are processed across hundreds or even thousands of manufacturing tools following highly complex workflows. We aim to develop optimization models and algorithms to improve wafer processing sequences across semiconductor manufacturing tools, with the objectives of reducing cycle times and lowering work-in-progress (WIP) inventory, thereby increasing throughput and overall manufacturing efficiency.b) Automated Material Handling System (AMHS) Scheduling: In modern semiconductor fabs, AMHS, typically realized through Overhead Hoist Transport (OHT), is responsible for transferring wafers between manufacturing tools. Given the massive production scale, the AMHS must handle tens of thousands of transport requests every day, ensuring timely and reliable delivery of wafers to the correct tools. Our research seeks to develop efficient and adaptive scheduling algorithms for AMHS systems to significantly reduce transport delays, improve material flow, and enhance coordination between logistics and manufacturing processes.c) Digital Twin System: We aim to construct a digital twin framework that integrates real-time data and simulation models to mirror the physical manufacturing and logistics systems. This enables performance monitoring, predictive analysis, and the evaluation of scheduling strategies in a virtual environment, thereby supporting more robust and adaptive decision-making in semiconductor manufacturing.
Responsibilities:a) Closely collaborate with researchers in the group to develop efficient algorithms for the above problems.b) Deliver research outcomes to our industry partners, to support practical applications in semiconductor manufacturing.c) Write and submit high-quality academic papers.
Qualifications：a) Applicants must have obtained a bachelor's or master's degree in Automation, Computer Science, Management Science, Industrial Engineering, or other related fields, or currently be a PhD student.b) Applicants must be proficient in at least one programming language (e.g., C++, Python) and meet at least one of the following criteria:
- Familiarity with modeling and common solution methods for scheduling and path planning problems, and experience with commercial solvers such as CPLEX or GUROBI. Prior experience in publishing academic papers is preferred.
- Strong programming skills, with preference given to those with software development experience or industry work experience.
Application: send your CV and a cover letter to Dr. Anbang Liu (anbang@hku.hk) and cc to Prof. Lin (shaoclin@hku.hk).
