Senior Engineer​/Scientist – CMP Fundamentals, Modeling & Simulation

Key Responsibilities:

• Technical Leadership & Scientific Problem Solving
• Lead complex technical investigations requiring deep understanding of CMP fundamentals, material behavior, and process interactions.
• Apply rigorous science-based methodologies to solve short- and long-term technology challenges.
• Partner with research, applications, product development, manufacturing, and data science teams to accelerate innovation.

Simulation Reliability, Validation & Capability Development:

• Build complex, physics-based simulation models from first principles
• Optimize model architecture, meshing strategy, solver settings, and runtime efficiency to enable reliable and practical use in product development.
• Evaluate model assumptions, governing equations, constitutive relationships, material models, and boundary conditions to ensure simulations accurately represent physical behavior.
• Verify model accuracy through numerical checks, experimental validation, calibration, and uncertainty assessment.

Experimental Research & Characterization:

• Design validation experiments in partnership with scientists and engineers.
• Develop methods for model calibration and parameter identification.
• Leverage characterization tools and experimental resources to improve understanding of polishing mechanisms.
• Serve as an internal expert in texture characterization, material behavior, and performance-driving mechanisms.
• Correlate simulation results with laboratory observations and customer outcomes.

CMP Fundamentals Development:

• Build fundamental understanding of interactions between:
  • Pad materials and structure
  • Solid Mechanics
  • Fluid mechanics
  • Contact mechanics
  • Tribology and wear
  • Semiconductor process operating conditions
  • Translate scientific understanding into actionable design guidance and product development recommendations.
• Develop and manage collaborations with internal and external technical experts.
• Communicate findings through technical reports, presentations, customer interactions, patents, and publications.

Required Qualifications:

• Master's degree or Ph.D. in Mechanical Engineering, Chemical Engineering, Materials Science, Physics, Applied Mathematics, or related discipline.
• Less than 5 years of industrial experience applying scientific and engineering principles to solve complex technical problems.
• Demonstrated experience in mathematical modeling of physical systems and experimental method development.
• Strong background in computational modeling, data analysis, and scientific computing.
• Proficiency with MATLAB, Python, Maple, or similar technical computing environments.
• Expert knowledge inatleastsome ofthe following areas:
  • Transport phenomena
  • Fluid mechanics
  • Polymer mechanics
  • Contact mechanics
  • Fracture mechanics
  • Tribology
  • Surface characterization
  • Materials characterization
  • Comfortable working across theory, simulation, experimentation, and characterization.
  • Strong interpersonal and collaboration skills with multidisciplinary technical teams

Desired

Skills:

• Familiarity with CMP consumables, processes, and polishing mechanisms.
• Understanding of polyurethane structure-property-performance relationships.
• Experience with:
  • Finite Element Analysis (FEA/FEM)
  • Computational Fluid Dynamics (CFD)
  • Multiphysics Simulation
  • Model Verification & Validation (V&V)
  • Uncertainty Quantification
  • Sensitivity Analysis
  • Hands-on experience with:
  • ANSYS
  • Abaqus
  • COMSOL
  • High Performance Computing (HPC)
• Experience evaluating:
  • Model assumptions, governing equations, and constitutive relationships
  • Numerical stability, convergence behavior, and solution robustness
  • Mesh and time-step independence studies
  • Boundary condition selection and sensitivity analyses
  • Model calibration, parameter estimation, and uncertainty quantification
  • Solver performance, computational efficiency, and runtime optimization
  • Verification and validation (V&V) methodologies for predictive engineering models
  • Correlation of simulation predictions with experimental observations and physical behavior