Staff R&D Scientist

What matters most

• Advanced X-ray Metrology Algorithm Development

  The core responsibility is designing and implementing algorithms for CD-SAXS, XRD, and XRR data analysis. You must demonstrate proficiency in solving inverse problems, forward simulation, and profile reconstruction to improve measurement accuracy and computational efficiency.

• Cross-Functional Research Integration

  You will bridge the gap between research concepts and production-ready solutions. This requires collaborating with application scientists, software engineers, and product management to ensure that your models and data fusion frameworks are scalable and reliable for semiconductor manufacturing environments.

• Data Fusion and Modeling Innovation

  You are expected to create frameworks that integrate data from multiple metrology sources (e.g., CD-SAXS, XRD, OCD). This involves applying machine learning and physics-based modeling to extract meaningful parameters from complex datasets.

Likely challenges to think through

• Bridging Physics-Based Models with Machine Learning

  A significant challenge is balancing traditional physics-based forward simulations with modern machine learning approaches. Prepare to discuss how you validate ML-driven predictions against physical constraints to ensure robustness in a high-precision semiconductor context.

• Transitioning Research to Production

  Moving from a prototype to a production-ready tool involves strict reliability and performance requirements. Prepare to explain your process for verifying algorithm performance and how you handle the iterative feedback loops inherent in a fast-paced R&D environment.

• Global Collaboration and Communication

  Operating across Singapore and the USA requires effective communication of complex technical concepts to diverse stakeholders. Be prepared to demonstrate how you present technical findings to both specialized peers and non-technical management to drive project alignment.

Topics to prepare

• Inverse Problem Solving in Metrology

  The role requires solving inverse problems for profile reconstruction in X-ray metrology. Exercise: Review literature on regularization techniques for ill-posed inverse problems in scattering data and practice mapping physical parameters to measured diffraction patterns.

• Multi-Sensor Data Fusion

  The position involves creating frameworks to combine data from CD-SAXS, XRD, and OCD. Exercise: Study statistical methods for sensor fusion, such as Bayesian inference or Kalman filtering, to integrate heterogeneous data sources for improved parameter extraction.

• Physics-Based Forward Simulation

  Developing forward simulations for X-ray techniques is a core requirement. Exercise: Implement a basic forward model for a diffraction experiment and validate it against synthetic data to demonstrate proficiency in physical parameter extraction.

Skills to revise

• Algorithm Development in Python or C++

  You must transition research concepts into production-ready code. Exercise: Refactor a prototype script into a modular, documented, and performance-optimized function, focusing on computational efficiency for large-scale data analysis.

• Machine Learning for Metrology

  The role utilizes Scikit-learn and TensorFlow for characterization. Exercise: Build a regression model using Scikit-learn to predict physical parameters from simulated X-ray diffraction features, comparing performance against traditional optimization-based fitting.

• Advanced X-ray Technique Analysis

  Hands-on experience with CD-SAXS and XRD is required. Exercise: Review standard data analysis workflows for CD-SAXS and XRD, specifically focusing on profile fitting and identifying common artifacts in semiconductor microelectronics characterization.

• Prepare a technical case study on inverse problems

  Develop a concise presentation or document detailing your experience with solving inverse problems in a scientific context. Focus on your methodology for parameter extraction, profile fitting, or forward simulation, specifically highlighting how you handled noise, computational efficiency, and validation against experimental data.

• Demonstrate expertise in X-ray metrology and physics

  Review your past research or projects involving CD-SAXS, XRD, or XRR. Be prepared to discuss the underlying physics of these techniques and how you have applied them to characterize microelectronics or similar nanostructures, emphasizing your ability to translate physical phenomena into mathematical models.

• Showcase programming and machine learning proficiency

  Prepare code samples or project summaries that demonstrate your ability to use Python, MATLAB, or C++ for scientific computing. Highlight specific instances where you utilized Scikit-learn or TensorFlow to improve algorithm performance or implement data fusion frameworks.

• Formulate questions regarding cross-functional collaboration

  Prepare thoughtful questions about how the R&D team interacts with application scientists and software engineering teams. Focus on the transition process from research prototypes to production-ready solutions to show your interest in the full product lifecycle.

• Review semiconductor domain knowledge

  If you lack direct semiconductor industry experience, research the current challenges in semiconductor metrology, such as yield optimization and nanometer-scale characterization. Be ready to articulate how your existing skills in signal processing, statistics, or optimization are transferable to these specific industry problems.