Computational Lithography Engineer

Project

Work done for a product or program.

• Develop and validate rigorous and compact optical imaging models based on Abbe and Hopkins formulations, transmission cross coefficients and SOCS kernel decomposition for DUV and EUV systems.
• Build resist and etch models including acid diffusion, deprotection kinetics, compact resist thresholds and calibrated etch bias models, and quantify their predictive limits on new materials.
• Model EUV-specific physics including mask 3D effects, absorber shadowing, flare, out-of-band radiation and pupil aberration sensitivity for high-NA anamorphic imaging.
• Develop stochastic models for EUV photon shot noise, local CD uniformity, line edge roughness and stochastic failure probability, and turn them into yield-relevant failure rate predictions.
• Implement and tune inverse lithography technology and curvilinear mask optimisation algorithms, balancing wafer image quality against mask manufacturability and shot count.
• Run source-mask co-optimisation to derive freeform illumination pupils and matching mask designs, and hand the resulting source to the scanner and OPC teams.
• Accelerate simulation and optimisation workloads with GPU and distributed compute, profiling and re-architecting kernels so full-chip runs finish within tapeout windows.
• Develop machine learning surrogate models for lithography simulation, hotspot prediction and OPC acceleration, and prove their accuracy against rigorous simulation and wafer data.
• Build and maintain the model calibration infrastructure, test pattern gauge sets and metrology data pipelines that feed every production model.

Function

Work that keeps the discipline running: methods, tools, suppliers and people.

• Collaborate with ASML, Synopsys, Siemens EDA and imec on next-node patterning studies, and publish or present results at SPIE Advanced Lithography and internal technology reviews.

Project

1. Produce and hand over the role's key deliverables
   • Validated optical, resist, etch and mask 3D models released to the production OPC flow
   • Stochastic failure models and EUV local CD uniformity predictions per layer
   • ILT and source-mask optimisation algorithms and their production deployment recipes
   • GPU-accelerated simulation kernels and runtime benchmarking reports
   • Model calibration test pattern sets, gauge definitions and metrology data pipelines
   • Next-node patterning feasibility studies and technical papers or internal white papers
2. Run research projectsGeneral
   • Define hypotheses and experiments
   • Build test structures or models
   • Analyse and report the results
3. Transfer resultsGeneral
   • Hand findings over to development
   • Support early adoption

Function

1. Stay currentGeneral
   • Review the literature
   • Work with universities and consortia
2. Protect and shareGeneral
   • File invention disclosures
   • Publish where allowed