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Area of Science:

  • Materials Science
  • Nanotechnology
  • Computer Science

Background:

  • Scanning electron microscopy (SEM) provides nanoscale imaging but needs vacuum and sample coating.
  • Optical super-resolution (OSR) microscopy offers an alternative but lacks SEM's resolution and contrast for certain applications.
  • Existing methods struggle to bridge the resolution and detail gap between OSR and SEM for complex samples like microchips.

Purpose of the Study:

  • To develop a deep learning approach for transforming OSR images into SEM-like images.
  • To overcome the limitations of SEM, such as vacuum and coating requirements, for nanoscale imaging of chip samples.
  • To enhance the visualization of nanoscale microstructures in chip samples using OSR and AI.

Main Methods:

  • A custom scanning superlens microscopy (SSUM) system was used to acquire OSR images with ~80 nm resolution and a 2 μm depth-of-field.
  • A cycle-consistent generative adversarial network (CycleGAN) was trained on paired OSR and SEM images.
  • The trained CycleGAN model was applied to OSR images of silicon wafer samples to generate SEM-like reconstructions.

Main Results:

  • The SSUM system enabled OSR imaging without vacuum or coatings, visualizing multilayer chip structures.
  • The deep learning algorithm enhanced nanoscale details in OSR images, improving visibility.
  • Reconstructed images showed a 1.64 dB increase in mean peak signal-to-noise ratio (PSNR) compared to input OSR images.
  • Qualitative assessment confirmed high structural detail in the generated SEM-like images, suitable for chip-level applications.

Conclusions:

  • The deep learning-based transformation of OSR to SEM-like images overcomes key SEM limitations while maintaining nanoscale resolution.
  • This technique offers a promising alternative for advanced chip manufacturing and inspection, particularly where SEM constraints are problematic.
  • The combined SSUM and deep learning approach provides enhanced nanoscale imaging capabilities for semiconductor analysis.