CHIP

MaCHIne-Learning-assisted Ptychographic nanotomography

Started
June 1, 2023
Status
In Progress
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Abstract

The high penetration and small wavelength of X-ray photons provides unsurpassed capabilities to probe matter down to the atomic scale. Prominent examples are given in the wide-spread impact of X-ray tomography, X-ray crystallography, and X-ray spectroscopy. Nanoscale, non-destructive imaging with X-rays can probe representative volumes and has numerous applications in energy research, materials science, biology, and medicine.

However, current measurement strategies largely follow the Nyquist-Shannon theorem (1949), which requires dense sampling in all dimensions and therewith ensures that no signal is missed in the measurement. For many scientific cases, there are patterns, anisotropy, heterogeneity, and sparsity that can be leveraged to optimize sampling for 3D imaging. Reducing and optimizing the number of measurements for X-ray nanoimaging is crucial because access is scarce at large-scale facilities, but more fundamentally, the ultimate limit for X-ray tomography resolution is given by the tolerance of the sample to radiation-induced damage. Viewed like this the total dose that the sample can tolerate can be seen as a limited budget, which, if optimized, can lead to resolution and quality that would be otherwise inaccessible.

The project has two main goals. The first is to develop, test, and implement data-driven measurement techniques that optimize sampling during the experiment. From a quick low-resolution measurement an optimal high-resolution sampling strategy is determined and fed back to the experiment. The tools developed here are of general applicability to samples with anisotropic information distribution. The second goal is the leveraging of prior information about the sample in the measurement and reconstruction. For this we will focus on imaging of integrated circuits (IC) with (<20 nm) FinFET technology. For such samples we have detailed global distribution system (GDS) layout, which can be used to further reduce the number of measurements needed.

People

Collaborators

SDSC Team:
Benjamin Béjar Haro
Johannes Kirschner
Luis Barba Flores

PI | Partners:

PSI, Computational X-ray Imaging Group and EPFL, Institute of Physics, Computational X-ray Imaging Laboratory:

  • Prof. Dr. Manuel Guizar-Sicairos
  • Dr. Mirko Holler
  • Dr. Tomas Aidukas

More info

EPFL, Laboratory for Topological Matter:

  • Prof. Dr. Gabriel Aeppli

More info

University of Southern California, Viterbi School of Engineering:

  • Prof. Dr. Tony Levi
  • M.Sc. Walter Unglaub

More info

description

Motivation

Computed tomography aims to create a 3D reconstruction from 2D projection images that are obtained from multiple X-ray scans of the object of interest (Figure 1). The X-ray scans are typically performed according to a fixed (uniform) design with limited adaptation to the scanned object. Adaptive experimental design and learning methods are a promising approach to improve the data efficiency and limiting the total amount of X-rays required for the reconstruction. In particular, learning methods can be trained on prior data, thereby leveraging structure of the data distribution in a more efficient way than traditional reconstruction methods.

Proposed Approach / Solution

The SDSC is leading the development of active learning and adaptive experimental design algorithms for X-ray tomography and ptychography as well as developing novel reconstruction algorithms using computer vision and generative modelling. The goal is to scale the approach to high-resolution 3D imaging of integrated circuits (Figure 2) and deploy it on PSI’s state-of-the-art X-ray imaging facilities.

Impact

Increasing data efficiency of computed tomography saves valuable measurement time and limits the X-ray exposure to the scanned object. Faster reconstruction methods facilitate online evaluation of the experiment, thereby providing valuable feedback to the practitioners. Beyond the scope of the project, we expect the techniques to be relevant for a wider range of scientific questions, including medical applications.

Figure 1: A typical X-ray tomography setup with a rotating sample. Credit: Donnelly et al. (2017).
Figure 2: Tomographic reconstruction of a computer chip, showing different levels of detail. Credit: Holler et al. (2019).

Gallery

Annexe

Additional resources

Bibliography

  1. Guizar-Sicairos, Manuel, and Pierre Thibault. "Ptychography: A solution to the phase problem." Physics Today 74.9 (2021): 42-48. https://doi.org/10.1063/pt.3.4835
  2. Dierolf, Martin, Andreas Menzel, Pierre Thibault, Philipp Schneider, Cameron M. Kewish, Roger Wepf, Oliver Bunk, and Franz Pfeiffer. "Ptychographic X-ray computed tomography at the nanoscale." Nature 467, no. 7314 (2010): 436-439. https://doi.org/10.1038/nature09419
  3. Barbano, R., Leuschner, J., Antorán, J., Jin, B., & Hernández-Lobato, J. M. (2022). “Bayesian experimental design for computed tomography with the linearised deep image prior”. arXiv preprint arXiv:2207.05714. https://doi.org/10.48550/arXiv.2207.05714
  4. Holler, M., Odstrcil, M., Guizar-Sicairos, M., Lebugle, M., Müller, E., Finizio, S., Tinti, G., David, C., Zusman, J., Unglaub, W. and Bunk, O., 2019. Three-dimensional imaging of integrated circuits with macro-to nanoscale zoom. Nature Electronics, 2(10), pp.464-470. https://doi.org/10.1038/s41928-019-0309-z
  5. Donnelly, Claire, Manuel Guizar-Sicairos, Valerio Scagnoli, Sebastian Gliga, Mirko Holler, Jörg Raabe, and Laura J. Heyderman. "Three-dimensional magnetization structures revealed with X-ray vector nanotomography." Nature 547, no. 7663 (2017): 328-331. https://doi.org/10.1038/nature23006

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