Postdoctoral Researcher in Data-driven X-ray Nanoimaging Semiconductor Metrology

EPFL - Swiss Federal Institute of Technology Lausanne

Villigen, Switzerland

This position focuses on high-throughput imaging of static structural defects, while the companion position focuses on time-resolved imaging of dynamic processes. Each postdoc will lead their own independent research direction, with an opportunity for close collaborative work.

You will develop an acquisition and reconstruction framework that uses the known geometry, composition, and layout regularity of semiconductor devices as prior information to detect deeply buried defects from the smallest possible number of X-ray measurements. Our pipeline reconstructs each projection from thousands of diffraction patterns, followed by volume reconstruction from projections measured at many rotation angles. These are two inverse problems, each with its own sampling requirements.

• What is the minimum number of diffraction patterns and projections needed to identify a defect?
• Can we detect defects from diffraction patterns alone, bypassing projection reconstruction?
• Can we reliably identify defects without ever resolving them?
• What is the best way to incorporate the known design information into the imaging process?

The primary focus will be on algorithm development, followed by their demonstration at synchrotron experiments. You will start working with real already existing experimental datasets and will have the opportunity to extend our established reconstruction pipeline, and integrate with new data acquisition methods. You will also get a chance to perform experiments at the cSAXS beamline of the upgraded Swiss Light Source synchrotron, as well as other facilities around the world.

• PhD in physics, engineering, computer science, or a related field.
• Strong scientific programming in Python and experience with GPU processing of large-scale datasets.
• Experience with inverse problems and 3D reconstruction methods for tomography, laminography, or a closely related modality.
• Excellent written and oral communication skills in English.

• Coherent diffractive imaging, especially ptychography.
• Sparse sensing, optimization, or Bayesian experimental design.
• Machine learning for imaging.
• Synchrotron experiment experience.
• Semiconductor devices or metrology.