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Senior Engineer / Scientist - CMP Fundamentals, Modeling & Simulation

Description

The CMP Fundamentals team is seeking an experienced scientist or engineer to serve as a technical leader in the development, validation, and application of predictive modeling capabilities supporting next-generation CMP consumables and processes.

This position will combine rigorous scientific investigation, advanced characterization, experimental research, and computational modeling to build fundamental understanding of the relationships between consumable properties, process conditions, and polishing performance.

A keyobjectiveof this role is toestablishconfidence in simulation-driven decision making by ensuring models are physically meaningful, numerically reliable, experimentallyvalidated, and aligned with business needs. The successful candidate will define what should be modeled, challenge assumptions, improve model fidelity, quantify uncertainty, and drive the long-term simulation capability roadmap for CMPT.

This role is intended to develop a long-term CMP technical expert capable of owning complex technical problem statements, mentoring future scientists and engineers, and serving as a critical technical partner for product development, customer support, and innovation efforts.

Approximately 20% travel may berequiredto customer sites, development facilities, collaborator locations, and technical review meetings.This position isultimately intendedto be basedonsitein Newark, Delaware. To support recruitment of exceptional candidates, flexibility may be provided during theinitialonboarding and transition period. Candidates should be willing torelocateto the Newark area and work onsite as the long-term expectation for the role.

Key Responsibilities

Technical Leadership & Scientific Problem Solving

• Lead complex technical investigations requiring deep understanding of CMP fundamentals, material behavior, and process interactions.

• Own and define technical problem statements supporting product development and customer needs.

• 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

• Optimizemodel architecture, meshing strategy, solver settings, and runtime efficiency to enable reliable and practical use in product development.

• Evaluatemodel assumptions, governing equations, constitutive relationships, material models,andboundary conditions toensure simulations accuratelyrepresentphysical behavior.

• Verify model accuracy through numerical checks, experimental validation, calibration, and uncertainty assessment.

• Define and prioritize the simulationcapabilityroadmap, including new predictive modeling approaches that link consumable properties to polishing performance.

• Serve as the internal technical authority for simulation and predictivemodeling.

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.

Mentorship & Collaboration

• Demonstrate strong mentorshipand trainscientists, engineers, and technicians.

• Guideothersactively, sharing knowledge openly, providing constructive feedback

• Fostera collaborative environment where scientists and engineers canlearnwithgrowthmindset.

• 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.

• 5-10+ 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.

• Proficiencywith 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 ofpolyurethane 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 PerformanceComputing (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

• Demonstrated record of technical innovation through patents, publications, or product development contributions.

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#Newark,DE