Regularized pseudo-phase imaging for inspecting and sensing nanoscale features

Abstract

Recovering tiny nanoscale features using a general optical imaging system is challenging because of poor signal to noise ratio. Rayleigh scattering implies that the detectable signal of an object of size d illuminated by light of wavelength λ is proportional to d 6 /λ 4 , which may be several orders of magnitude weaker than that of additive and multiplicative perturbations in the background. In this article, we solve this fundamental issue by introducing the regularized pseudo-phase, an observation quantity for polychromatic visible light microscopy that seems to be more sensitive than conventional intensity images for characterizing nanoscale features. We achieve a significant improvement in signal to noise ratio without making any changes to the imaging hardware. In addition, this framework not only retains the advantages of conventional denoising techniques, but also endows this new measurand (i.e., the pseudo-phase) with an explicit physical meaning analogous to optical phase. Experiments on a NIST reference material 8820 sample demonstrate that we can measure nanoscale defects, minute amounts of tilt in patterned samples, and severely noisepolluted nanostructure profiles with the pseudo-phase framework even when using a low-cost bright-field microscope.

Ghost Imaging with Deep Learning for Position Mapping of Weakly Scattered Light Source

“…A line-scanning laser has been applied to improve detection speed [9,10]. Both the intensity and phase information of scattered light can be utilized with good robustness [11][12][13]. Localized physical phenomena of evanescent waves or Raman scattering allow the detection of sub-micron defects [14,15].…”

All-dielectric concentration of electromagnetic fields at the nanoscale: the role of photonic nanojets

“…49 In a macroscale imaging system, a high-NA objective lens enables optical trapping and sensing of nanoscale objects. 50,51 Analogously, the microlens effect of cells make it possible to use a single cell to trap, image, and sense nanoparticles as was successfully demonstrated very recently. 52 A signicant difference to conventional optical imaging is that the single cell can be operated in contact mode such that the possible evanescence-propagation coupling can facilitate super-resolution imaging.…”

“…However, the imaging resolution beyond the Rayleigh limit via the contacted "microsphere lens," makes the PNJ-assisted imaging signicantly different to any known conventional far-eld-based imaging modalities. 51,167,168 Specically, it has been reported that a solid immersed lens (nanolens) with n ¼ 1.5 can resolve features beyond the diffraction limit. 169 These nanolenses, in contrast to geometrical optics lenses, exhibit curvilinear trajectories of light, resulting in remarkably short near-eld focal lengths.…”

Machine learning techniques applied for the detection of nanoparticles on surfaces using coherent Fourier scatterometry

“…Finally, taking into account the growing amount of data, techniques such as CFS lack the tools of being able to process raw data sets automatically and effectively. Recently, to overcome the challenges of detection and classification of smaller particles, machine learning methods, including the regularized matrix-based imaging framework [19], principal component analysis [20], and convolutional neural networks [21] were applied to image-based defect detection.…”