PhD Position in Computational X-ray Imaging and Semiconductor Metrology

PhD Position in Computational X-ray Imaging and Semiconductor Metrology Mission

The newly established Laboratory for Nanoscale X-ray Metrology is looking for a motivated PhD student to develop computational and experimental methods for high-resolution 3D imaging. In this role, you will help establish new X-ray microscopy performance benchmarks by pushing the limits of imaging resolution and sample thickness achievable for semiconductor applications. To achieve this, the project will focus on the following topics:

• Depth-of-Field Limitations:
  At 1 nm resolution, the depth of field shrinks to just a hundred nanometers, yet the high X-ray penetration allows imaging of samples that are several orders of magnitude thicker. The project will develop an extended depth-of-field reconstruction framework for thick 3D samples.
• Mismatch Between Theory and Experiment:
  Reconstruction methods are based on idealized image formation models, which break down under extreme imaging conditions. Achieving high-performance imaging requires improving the models and developing robust computational optimization algorithms, to account for issues such as experimental nanoscale vibrations.
• High-Performance 3D Imaging:
  Integrating these methods into tomography and laminography, and performing synchrotron-based experiments to visualize previously "unseen" features in modern semiconductor devices.

Project Description

The performance of X-ray microscopy has improved significantly over the last decade, enabling synchrotron-based X-ray imaging to resolve individual transistors in modern semiconductor devices. As semiconductor technology enters the new era of the 3D transistor, the need for non-destructive, high-resolution 3D imaging methods has never been greater. While X-ray microscopy holds the potential for nanometer-scale defect detection in 3D semiconductor devices, its imaging performance requires further breakthroughs.

Currently, the highest-performance microscopy tools utilize ptychography—a coherent diffractive lensless imaging technique that replaces the limitations of traditional lenses with sophisticated computational algorithms. However, achieving the ~1 nm 3D resolution required for next-generation semiconductor metrology remains a "grand challenge". At these extreme resolutions, massive photon requirements cause radiation damage, nanoscale mechanical instabilities become severe, and the projection approximation completely breaks down for thick samples.

• A fully funded 4-year PhD position with competitive salary.
• Core involvement in the SNSF Starting Grant project NEXUS-3D, working at the intersection of applied physics, high-performance computing, and semiconductor metrology.
• Opportunity to work together with leading synchrotron scientists and computational X-ray imaging experts.
• A unique research environment based at a newly upgraded 4th generation synchrotron.
• Support for professional development such as attending summer schools, international conferences and conducting experiments at synchrotrons around the world.

We are looking for a PhD candidate highly motivated to develop computational methods for lensless X-ray microscopy. This position is for you if you bring:

• Educational Background: A Master’s degree in Physics, Engineering, Mathematics, Computer Science, or a related field.
• Computational
  
  Skills:
  
  Strong Python proficiency with a drive to develop algorithms for large-scale data reconstruction.
• Lensless Imaging: A keen interest in mastering the physics of coherent diffractive imaging and solving inverse imaging problems (e.g., ptychography).
• Synchrotron Experiments:
  Enthusiasm for hands‑on research, such as conducting and designing synchrotron X‑ray imaging experiments.
• Teamwork & Culture:
  Fluent English and a collaborative, open‑minded attitude. You will be working in a diverse, international team spanning a broad range of cultures, genders, and backgrounds.
• Bonus:
  Prior experience with tomography, computational optimization, machine learning, coherent imaging, synchrotron experiments, or high-performance computing (HPC) is a plus.

Activity Rate : 100%

Contract Type:
PhD student

Duration: 4 years

Reference: 2224