TY - JOUR
T1 - Suppressing electron exposure artifacts
T2 - An electron scanning paradigm with Bayesian machine learning
AU - Hujsak, Karl
AU - Myers, Benjamin D.
AU - Roth, Eric
AU - Li, Yue
AU - Dravid, Vinayak P.
N1 - Funding Information:
The authors would like to acknowledge the help of Joe Nabity of JC Nabity Lithography Systems for his help modifying software to performthe randomsampling task. This work was supported by the NSF Graduate Research Fellowship Program and is based upon work supported by the Air Force Office of Scientific Research under Award No. FA9550-12-1-0280. This work made use of the EPIC facility (NUANCE Center-Northwestern University), which has received support from the MRSEC program (NSF DMR-1121262) at the Materials Research Center; the International Institute for Nanotechnology (IIN); the SHyNE Resource (NSF NNCI-1542205); and the State of Illinois, through the IIN.
Publisher Copyright:
© Microscopy Society of America 2016.
PY - 2016/8/1
Y1 - 2016/8/1
N2 - Electron microscopy of biological, polymeric, and other beam-sensitive structures is often hampered by deleterious electron beam interactions. In fact, imaging of such beam-sensitive materials is limited by the allowable radiation dosage rather that capabilities of the microscope itself, which has been compounded by the availability of high brightness electron sources. Reducing dwell times to overcome dose-related artifacts, such as radiolysis and electrostatic charging, is challenging due to the inherently low contrast in imaging of many such materials. These challenges are particularly exacerbated during dynamic time-resolved, fluidic cell imaging, or three-dimensional tomographic reconstruction - all of which undergo additional dosage. Thus, there is a pressing need for the development of techniques to produce high-quality images at ever lower electron doses. In this contribution, we demonstrate direct dose reduction and suppression of beam-induced artifacts through under-sampling pixels, by as much as 80% reduction in dosage, using a commercial scanning electron microscope with an electrostatic beam blanker and a dictionary learning in-painting algorithm. This allows for multiple sparse recoverable images to be acquired at the cost of one fully sampled image. We believe this approach may open new ways to conduct imaging, which otherwise require compromising beam current and/or exposure conditions.
AB - Electron microscopy of biological, polymeric, and other beam-sensitive structures is often hampered by deleterious electron beam interactions. In fact, imaging of such beam-sensitive materials is limited by the allowable radiation dosage rather that capabilities of the microscope itself, which has been compounded by the availability of high brightness electron sources. Reducing dwell times to overcome dose-related artifacts, such as radiolysis and electrostatic charging, is challenging due to the inherently low contrast in imaging of many such materials. These challenges are particularly exacerbated during dynamic time-resolved, fluidic cell imaging, or three-dimensional tomographic reconstruction - all of which undergo additional dosage. Thus, there is a pressing need for the development of techniques to produce high-quality images at ever lower electron doses. In this contribution, we demonstrate direct dose reduction and suppression of beam-induced artifacts through under-sampling pixels, by as much as 80% reduction in dosage, using a commercial scanning electron microscope with an electrostatic beam blanker and a dictionary learning in-painting algorithm. This allows for multiple sparse recoverable images to be acquired at the cost of one fully sampled image. We believe this approach may open new ways to conduct imaging, which otherwise require compromising beam current and/or exposure conditions.
KW - Bayesian machine learning
KW - dictionary learning
KW - dose reduction
KW - in situ imaging
KW - scanning electron microscopy
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U2 - 10.1017/S1431927616011417
DO - 10.1017/S1431927616011417
M3 - Article
C2 - 27456711
AN - SCOPUS:84979971904
VL - 22
SP - 778
EP - 788
JO - Microscopy and Microanalysis
JF - Microscopy and Microanalysis
SN - 1431-9276
IS - 4
ER -