Depth-Detection Methods for CNT Manipulation and Characterization in a Scanning Electron Microscope

Fatikow, Sergej; Eichhorn, Volkmar; Wich, Thomas; Sievers, Torsten; Hanssler, O.; Andersen, K.N.

doi:10.1109/icma.2007.4303514

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…A new funded European NanoHand (Micro-Nano System for Automatic Handling of Nano-Objects) aims to develop a system consisting of micro/ nano-based subsystems for automatic handling of nano objects like CNTs and nanowires (European Commission, 2007;Fatikow et al, 2006Fatikow et al, -2009Fatikow et al, 2007). A new funded European NanoHand (Micro-Nano System for Automatic Handling of Nano-Objects) aims to develop a system consisting of micro/ nano-based subsystems for automatic handling of nano objects like CNTs and nanowires (European Commission, 2007;Fatikow et al, 2006Fatikow et al, -2009Fatikow et al, 2007).…”

Section: Gripper-based 3d Nanohandlingmentioning

confidence: 99%

Robotic nanoassembly: current developments and challenges

Wang

Zhang

et al. 2011

IJCAT

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Robotic nanoassembly is an emerging field that deals with the controlled manipulation, handling and assembly of atoms, molecules and nano objects by robots for manufacturing of nano structures, devices and systems. Nanoassembly is expected to have revolutionary applications in almost all the scientific and technological areas. This paper presents a general review of nanoassembly by robots considering its current developments and challenges. It discusses scanning probe-based 2D nanomanipulation, gripper-based 3D nanohandling, object-oriented nanoassembly and hybrid nanoassembly techniques, which are the main topics of interest in the field. The challenging issues in robotic nanoassembly are outlined together with the topics.

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Section: Gripper-based 3d Nanohandlingmentioning

confidence: 99%

Robotic nanoassembly: current developments and challenges

Wang

Zhang

et al. 2011

IJCAT

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“…Therefore, accurate perception of micro/nano-objects has been shown to be essential for closed-loop micro/nano-manipulation and visual servoing, as laboratory-based experiments are often conducted under microscopic observations. Thus far, most of the micro/nano-scale operations are conducted by an operator using a manual joystick, keyboard 12 or a haptic device 13,14 . To develop a semi-or fully automatic micro/ nano-manipulation platform, three-dimensional (3D) pose estimation of the micro/nano-objects is needed, which relies on the microscopic imaging as feedback 15,16 .…”

mentioning

confidence: 99%

Micro-object pose estimation with sim-to-real transfer learning using small dataset

et al. 2022

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Three-dimensional (3D) pose estimation of micro/nano-objects is essential for the implementation of automatic manipulation in micro/nano-robotic systems. However, out-of-plane pose estimation of a micro/nano-object is challenging, since the images are typically obtained in 2D using a scanning electron microscope (SEM) or an optical microscope (OM). Traditional deep learning based methods require the collection of a large amount of labeled data for model training to estimate the 3D pose of an object from a monocular image. Here we present a sim-to-real learning-to-match approach for 3D pose estimation of micro/nano-objects. Instead of collecting large training datasets, simulated data is generated to enlarge the limited experimental data obtained in practice, while the domain gap between the generated and experimental data is minimized via image translation based on a generative adversarial network (GAN) model. A learning-to-match approach is used to map the generated data and the experimental data to a low-dimensional space with the same data distribution for different pose labels, which ensures effective feature embedding. Combining the labeled data obtained from experiments and simulations, a new training dataset is constructed for robust pose estimation. The proposed method is validated with images from both SEM and OM, facilitating the development of closed-loop control of micro/nano-objects with complex shapes in micro/nano-robotic systems.

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“…The degree of automation can be categorized into three levels: low-automated, semi-automated and fullyautomated. The low-automated level control is based on teleoperated systems which performances strongly depend on user skills and available information [7], [8]. The process is timeconsuming and often not repeatable because of the user-centered approach.…”

mentioning

confidence: 99%

Model Predictive Control With Obstacle Avoidance for Inertia Actuated AFM Probes Inside a Scanning Electron Microscope

Liang¹,

Morin²,

Cailliez³

et al. 2021

IEEE Robot. Autom. Lett.

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The Atomic Force Microscope (AFM) is a reliable tool 6 for 3D imaging and manipulation at the micrometer and nanometer 7 scales. When used inside a Scanning Electron Microscope (SEM), 8 AFM probes can be localized and controlled with a nanometer res-9 olution by visual feedback. However, achieving trajectory control 10 and obstacles avoidance is still a major concern for manipulation 11 tasks. We propose a Model Predictive Control (MPC) to address 12 these two issues while AFM probes are actuated by Piezoelectric 13 Inertia type Actuators (PIA). The novelty of this letter is that the 14 model of our MPC-based approach relies on a velocity map of PIAs.

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Depth-Detection Methods for CNT Manipulation and Characterization in a Scanning Electron Microscope

Cited by 7 publications

References 23 publications

Robotic nanoassembly: current developments and challenges

Robotic nanoassembly: current developments and challenges

Micro-object pose estimation with sim-to-real transfer learning using small dataset

Model Predictive Control With Obstacle Avoidance for Inertia Actuated AFM Probes Inside a Scanning Electron Microscope

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