The low-vacuum and low-accelerating-voltage modes are the most simple and practical ways to directly analyze poorly conductive samples in conventional scanning electron microscopy (SEM). However, structural feature information may disappear or be obscured in these imaging modes, making it challenging to identify and analyze some local microstructures of poorly conductive samples. To overcome this challenge, an enhanced visualization image acquisition method for samples with poor conductivity is proposed based on the image registration and multi-sensor fusion technology. Experiments demonstrate that the proposed method can effectively obtain enhanced visualization images containing clearer terrain information than the SEM source images, thereby providing new references for measuring and analyzing microstructures.

1.
S.
Pang
,
X.
Zhang
,
X.
Zhang
, and
Y.
Lu
, “
A magnification-continuous calibration method for SEM-based nanorobotic manipulation systems
,”
Rev. Sci. Instrum.
90
,
053706
(
2019
).
2.
S.
Pang
,
X.
Zhang
,
H.
Li
, and
Y.
Lu
, “
Edge determination improvement of scanning electron microscope images by inpainting and anisotropic diffusion for measurement and analysis of microstructures
,”
Measurement
176
(
14
),
109217
(
2021
).
3.
F.
Salvat-Pujol
and
J. S.
Villarrubia
, “
Conventional vs. model-based measurement of patterned line widths from scanning electron microscopy profiles
,”
Ultramicroscopy
206
,
112819
(
2019
).
4.
S.
Wachowski
and
A.
Mielewczyk-Gryń
, “
High resolution SEM imaging of non-conducting ceramics
,” in
15th Summer School on Crystal Growth
(Gdansk University of Technology,
2013
).
5.
I.
Jóźwik
,
J.
Jagielski
,
E.
Dumiszewska
,
M.
Kamiński
, and
U.
Kentsch
, “
Resistivity contrast imaging in semiconductor structures using ultra-low energy scanning electron microscopy
,”
Ultramicroscopy
228
,
113333
(
2021
).
6.
C.
Cong
,
G.
Ran
,
H.
Shang
, and
T.
Peng
, “
Characterization of non-conductive materials using field emission scanning electron microscopy
,” in
International Symposium on Precision Mechanical Measurements
(
SPIE
,
2016
).
7.
K.
Robertson
,
R.
Gauvin
, and
J.
Finch
, “
Application of charge contrast imaging in mineral characterization
,”
Miner. Eng.
18
(
3
),
343
352
(
2005
).
8.
R.
Gauvin
, “
Low voltage imaging and X-ray microanalysis in the FE-SEM
,”
Microsc. Microanal.
16
(
S2
),
626
627
(
2010
).
9.
J.
Liu
, “
High-resolution and low-voltage FE-SEM imaging and microanalysis in materials characterization
,”
Mater. Charact.
44
(
4–5
),
353
363
(
2000
).
10.
H.
Demers
,
N.
Brodusch
,
P.
Woo
, and
R.
Gauvin
, “
Origins and contrast of the electron signals at low accelerating voltage and with energy-filtering in the FE-SEM for high resolution imaging
,”
Microsc. Microanal.
21
(
S3
),
705
706
(
2015
).
11.
M.
Stevenskalceff
, “
Localised charging effects induced by low voltage SEM operation in non-conductive materials
,”
Microsc. Microanal.
9
(
S02
),
976
977
(
2003
).
12.
W.
Slowko
and
M.
Krysztof
, “
Signal detection and processing system for three-dimensional imaging of nonconductive surfaces in SEM
,”
Proc. SPIE
8902
,
890229
(
2013
).
13.
D.
Phifer
, “
Improving SEM imaging performance using beam deceleration
,”
Microsc. Today
17
(
4
),
40
49
(
2009
).
14.
A. O.
Karali
,
S.
Cakir
, and
T.
Aytac
, “
Multiscale contrast direction adaptive image fusion technique for MWIR-LWIR image pairs and LWIR multifocus infrared images
,”
Appl. Opt.
54
(
13
),
4172
4179
(
2015
).
15.
D. P.
Bavirisetti
,
G.
Xiao
, and
G.
Liu
, “
Multi-sensor image fusion based on fourth order partial differential equations
,” in
2017 20th International Conference on Information Fusion (Fusion)
(
IEEE
,
2017
), p.
1
9
.
16.
R.
Liu
,
J.
Liu
,
Z.
Jiang
,
X.
Fan
, and
Z.
Luo
, “
A bilevel integrated model with data-driven layer ensemble for multi-modality image fusion
,”
IEEE Trans. Image Process.
30
,
1261
1274
(
2021
).
17.
C. d.
Xing
,
Z. s.
Wang
,
Q.
Ouyang
, and
C.
Dong
, “
Method based on bitonic filtering decomposition and sparse representation for fusion of infrared and visible images
,”
IET Image Process.
12
(
12
),
2300
2310
(
2018
).
18.
B.
Cui
, “
Infrared and visible images fusion based on gradient bilateral filtering
,” in
2016 3rd International Conference on Systems and Informatics
(
ICSAI
,
2016
).
19.
Q.
Wang
,
S.
Li
,
H.
Qin
, and
A.
Hao
, “
Robust multi-modal medical image fusion via anisotropic heat diffusion guided low-rank structural analysis
,”
Inf. Fusion
26
,
103
121
(
2015
).
20.
N.
Marturi
,
S.
Dembele
, and
N.
Piat
, “
Fast image drift compensation in scanning electron microscope using image registration
,” in
Conference on Automation Science and Engineering
(
IEEE
,
2013
), p.
807
812
.
21.
G.
Sapiro
,
Geometric Partial Differential Equations and Image Analysis
(
Cambridge University Press
,
2001
).
22.
P.
Perona
and
J.
Malik
, “
Scale-space and edge detection using anisotropic diffusion
,”
IEEE Trans. Pattern Anal. Mach. Intell.
12
(
7
),
629
639
(
1990
).
23.
R. T.
Whitaker
and
J.
Cates
, “
Translational computer science at the scientific computing and imaging institute
,”
J. Comput. Sci.
52
,
101217
(
2020
).
24.
M.
Sonka
,
V.
Hlavác
, and
R.
Boyle
, “
Image understanding
,” in
Image Processing, Analysis and Machine Vision
(Springer,
1993
), pp.
316
372
.
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