Job Descriptions
- Develop and validate predictive CMP simulation models that accurately represent complex physical and chemical interactions, with emphasis on copper, organic dielectric materials, slurry chemistry, and pad mechanics.
- Leverage advanced simulation tools to evaluate material removal rates (MRR), topography evolution, defect formation mechanisms, and process non-uniformity.
- Collaborate with process engineers and material scientists to correlate simulation results with experimental data, accelerating learning cycles and improving root-cause analysis.
- Optimize key CMP parametersincluding pressure, platen speed, slurry formulation, and pad conditioningthrough simulation to meet global and local planarization requirements for dual-damascene structures.
- Interpret simulation outputs to identify risks such as erosion, dishing, and delamination at critical material interfaces, and provide data-driven recommendations to enhance process performance and yield.
- Design and execute simulation-based DOE studies, delivering clear technical reports and presentations to cross-functional teams and management.
- Stay updated on emerging CMP technologies, organic dielectric materials, and advanced modelling methodologies to ensure continuous improvement and competitiveness in semiconductor manufacturing.
Job Requirements
- Education: Bachelor's degree in Chemical Engineering, Materials Science, Mechanical Engineering, Physics, or a related discipline
- Experience: 35 years of relevant experience in process modeling or simulation within the semiconductor industry, preferably with exposure to CMP or similar surface-finishing technologies.
- Technical Expertise: Solid foundation in fluid dynamics, reaction kinetics, contact mechanics, and materials science principles applicable to CMP.
- Software Skills: Proficient in industry-standard simulation tools such as COMSOL Multiphysics and ANSYS Fluent/Mechanical, with programming or scripting capabilities in Python or MATLAB for data analysis and model development.
- CMP Modeling Exposure: Experience using dedicated CMP modeling software or proprietary internal simulation platforms.
- Statistical Knowledge: Familiarity with statistical tools and methodologies, including JMP or Minitab.
- HPC Experience: Comfortable working in high-performance computing (HPC) environments to support large-scale simulation workloads.
We regret that shortlisted candidates will be notified.
Name: Teoh Xue Ling
Registration No.: R22107190
EA License No.: 02C3423
