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3D multi-energy deconvolution electron microscopy.
de Goede, Michiel; Johlin, Eric; Sciacca, Beniamino; Boughorbel, Faysal; Garnett, Erik C.
Afiliação
  • de Goede M; FOM Institute AMOLF, Amsterdam, 1098 XG, The Netherlands.
  • Johlin E; FOM Institute AMOLF, Amsterdam, 1098 XG, The Netherlands.
  • Sciacca B; FOM Institute AMOLF, Amsterdam, 1098 XG, The Netherlands.
  • Boughorbel F; FEI Company, Eindhoven, 5651 GG, The Netherlands. garnett@amolf.nl.
  • Garnett EC; FOM Institute AMOLF, Amsterdam, 1098 XG, The Netherlands.
Nanoscale ; 9(2): 684-689, 2017 Jan 05.
Article em En | MEDLINE | ID: mdl-27957576
Three-dimensional (3D) characterization of nanomaterials is traditionally performed by either cross-sectional milling with a focused ion beam (FIB), or transmission electron microscope tomography. While these techniques can produce high quality reconstructions, they are destructive, or require thin samples, often suspended on support membranes. Here, we demonstrate a complementary technique allowing non-destructive investigation of the 3D structure of samples on bulk substrates. This is performed by imaging backscattered electron (BSE) emission at multiple primary beam energies - as the penetration depth of primary electrons is proportional to the beam energy, depth information can be obtained through variations in the beam acceleration. The detected signal however consists of a mixture of the penetrated layers, meaning the structure's three-dimensional geometry can only be retrieved after deconvolving the BSE emission profile from the observed BSE images. This work demonstrates this novel approach by applying a blind source separation deconvolution algorithm to multi-energy acquired BSE images. The deconvolution can thereby allow a 3D reconstruction to be produced from the acquired images of an arbitrary sample, showing qualitative agreement with the true depth structure, as verified through FIB cross-sectional imaging.

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Qualitative_research Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Qualitative_research Idioma: En Ano de publicação: 2017 Tipo de documento: Article