This article is the first step in the development of a hybrid metrology combining AFM and SEM techniques for measuring the dimensions of a nanoparticle population in 3D space ( X , Y , Z ). This method exploits the strengths of each technique on the same set of nanoparticles. AFM is used for measuring the nanoparticle height and the measurements along X and Y axes are deduced from SEM images. A sampling method is proposed in order to obtain the best deposition conditions of SiO ₂ and gold nanoparticles on mica or silicon substrates. Only the isolated nanoparticles are taken into account in the histogram of size distribution. Moreover, a semi-automatic Matlab routine has also been developed to process the AFM and SEM images, measure and count the nanoparticles. This routine allows the user to exclusively select the isolated nanoparticles through a control interface. The measurements have been performed on spherical-like nanoparticles to test the method by comparing the results obtained with both techniques.

The size characterization of particles present in the form of agglomerates in images measured by scanning electron microscopy (SEM) requires a powerful image segmentation tool in order to properly define the boundaries of each particle. In this work, we propose to use an algorithm from the deep statistical learning community, the Mask-RCNN, coupled with transfer learning to overcome the problem of generalization of the commonly used image processing methods such as watershed or active contour. Indeed, the adjustment of the parameters of these algorithms is almost systematically necessary and slows down the automation of the processing chain. The Mask-RCNN is adapted here to the case study and we present results obtained on titanium dioxide samples (non-spherical particles) with a level of performance evaluated by different metrics such as the DICE coefficient, which reaches an average value of 0.95 on the test images.

The pursuit of sculpting materials at increasingly smaller and deeper scales remains a persistent subject in the field of micro- and nanofabrication. Anisotropic deep-reactive ion etching of silicon at cryogenic temperatures (cryo-DRIE) was investigated for fabricating arrays of vertically aligned Si nanowires (NWs) of a large range of dimensions from micrometers down to 30 nm in diameter, combined with commonly used wafer-scale lithography techniques based on optical, electron-beam, nanoimprint, and nanosphere/colloidal masking. Large selectivity of ∼100 to 120 and almost 700 was found with resists and chromium hard masks, respectively. This remarkable selectivity enables the successful transfer of patterned geometries while preserving spatial resolution to a significant extent. Depending on the requirements by applications, various shapes, profiles, and aspect ratios were achieved by varying process parameters synchronously or asynchronously. High aspect ratios of up to 100 comparable to the best result by metal-assisted wet-chemical etching and sub-μm trenches by DRIE were obtained with NW diameter of 200 nm, at an etch rate of ∼4 μm/min without being collapsed. At the same time, low surface roughness values were maintained on the NW top, sidewall, and bottom surface of ∼0.3, ∼13, and ∼2 nm, respectively, as well as high pattern fidelity and integrity, which were measured using angle-resolved Fourier microscopy, combined atomic force, and scanning electron microscopy on selected NWs. This work establishes the foundation in the controllable development of Si nanoarchitectures, especially at sub-100 nm structures, for energy-harvesting and storage, damage-free optoelectronics, quantum, photovoltaics, and biomedical devices.

At this time, there is no instrument capable of measuring a nano-object along the three spatial dimensions with a controlled uncertainty. The combination of several instruments is thus necessary to metrologically characterize the dimensional properties of a nano-object. This paper proposes a new approach of hybrid metrology taking advantage of the complementary nature of atomic force microscopy (AFM) and scanning electron microscopy (SEM) techniques for measuring the main characteristic parameters of nanoparticle (NP) dimensions in 3D. The NP area equivalent, the minimal and the maximal Feret diameters are determined by SEM and the NP height is measured by AFM. In this context, a kind of new NP repositioning system consisting of a lithographed silicon substrate has been specifically developed. This device makes it possible to combine AFM and SEM size measurements performed exactly on the same set of NPs. In order to establish the proof-of-concept of this approach and assess the performance of both instruments, measurements were carried out on several samples of spherical silica NP populations ranging from 5 to 110 nm. The spherical nature of silica NPs imposes naturally the equality between their height and their lateral diameters. However, discrepancies between AFM and SEM measurements have been observed, showing significant deviation from sphericity as a function of the nanoparticle size.