Postdoctoral Researcher in Time-resolved X-ray Nanoimaging for Semiconductor Reliability

Postdoctoral Researcher in Time-resolved X-ray Nanoimaging for Semiconductor Reliability

EPFL - Swiss Federal Institute of Technology Lausanne

Villigen, Switzerland

Mission

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 the industry moves to 3D transistor architectures and stacked, hybrid-bonded devices, it becomes important to study the initiation and propagation of defects due to thermal and electrical phenomena (e.g., dielectric breakdown or electromigration). Compared to other imaging modalities such as electron microscopy, X-ray microscopy is non-destructive and hence ideally suited for in-situ and operando 3D imaging. However, at the sufficient imaging resolutions (sub-20 nm) measurements become slow, and because dose accumulates with every repeated measurement, radiation damage sets a hard limit on how long a process can be followed.

During conventional 3D imaging, the goal is to resolve every single voxel of the 3D volume. However, the underlying dynamic changes can be temporally sparse and highly localized, meaning that only a small fraction of the sample voxels need to be re-measured to detect the change, rather than the complete 3D volume. Microchip samples are also highly structured, with known design rules, which can enable us to more easily detect sparse dynamic changes with as few measurements as possible. The newly established Laboratory for Nanoscale X-ray Metrology is looking for a motivated postdoctoral researcher to develop time-resolved imaging methods and apply them to semiconductor reliability problems.

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

Main duties and responsibilities

You will develop an acquisition and reconstruction framework that exploits sparsity of the underlying dynamics and microchip sample structure to detect nanoscale sample changes 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 that can be significantly relaxed through sparsity.

The questions we want to answer:

• How temporal and structural sparsity can be incorporated into the 3D dynamics reconstruction model?
• How sparse can a measurement be and still capture a high-resolution dynamic event?
• How do temporal resolution, spatial resolution, and radiation dose trade against one another, and what semiconductor dynamics can be observed?

The primary focus will be on algorithm development, followed by their demonstration at synchrotron experiments. You will start working with an existing data acquisition and reconstruction pipeline which will be improved and extended for semiconductor imaging. 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.

Profile

We are looking for a person with:

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

We also value applications from people with the following experience (or similar):

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