SFA-AM - Strategic Focus Area Advanced Manufacturing
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Nano Assembly

Automated process flow for high-speed additive nanostructure assembly with integrated process control
Chiral: The nano factory
As an ETH spin-off rooted in the Nano Assembly project, Chiral is currently industrializing the results. We aim to serve customers with our unprecedented fabrication capability for nanocarbon devices. Together, we will unlock a new generation of applications such as quantum technology, IoT sensors, and more. Please watch our video, showing our wafer-level production chain in action. If you are interested please contact us at info@chiralnano.com or visit our website www.chiralnano.com.
Project
Background and Main Goal
Ultra-low power sensors are key components in mobile and autonomous systems, for the Internet of Things, and as functional elements in wearable systems for the Internet of Humans. Nanomaterials such as 1D nanotubes & nanowires and 2D materials have shown unique properties for ultra-sensitive, ultra-low power sensor applications. Single Walled Carbon Nanotubes (SWNT) devices have been successfully demonstrated for chemical and physical sensing at unprecedented low power consumption in the range of μW per sensor function.
Today, the biggest barrier for technology transfer or commercialization of SWNT or other nanomaterial sensors is the lack of an industrial manufacturing process for high device yield at low costs. The project aims to develop a new manufacturing process for nano electronic sensor systems, with functional nanostructures, demonstrated for Single Walled Carbon Nanotubes (SWNTs), but applicable beyond SWNTs.  It includes aspects of machine learning, digitalization and requires adapted device design to exploit full functionality. The goal is to increase today’s state-of-the-art speeds by a factor of 1000 and to demonstrate that a fabrication of 1’000 components per hour is feasible.
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Structure of the Nano-Assembly project with two key work packages (WP)
Scope of Research Activities
➜ Develop an integrated process and system for the assembly of suspended SWNTs in devices
➜ Develop machine learning algorithms for process automation, control, and self-optimization
➜ Evaluate and implement high speed optical characterization processes for SWNTs
Key Challenges and Technical Problems to Solve
➜ SWNT growth and integration of metallization
➜ Passivation of suspended SWNTs
➜ Speed of the optical characterization of SWNTs
➜ Accuracy of the mechanical assembly of nanostructures
➜ Machine learning methods to optimize data analysis
Achievements and demonstrators
Batch growth of suspended carbon nanotubes
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In a high-volume fabrication process for nanoelectronic devices, batch growth of high-quality nanostructures is the crucial first step. We have demonstrated the growth of 1000+ individual, suspended carbon nanotubes on a single growth substrate. This is a 50-fold improvement compared to previous work in the literature. A large-scale growth substrate, which hosts ~6000 cantilever pairs has been developed. In parallel, multi-component parameter optimization has been performed to optimize the density of cantilever pairs with exactly one suspended carbon nanotube.

Contact: Dr. Cosmin Roman
Involved research groups: ETH Zurich, Micro- and Nanosystems; ETH Zurich, Institute of Machine Tools and Manufacturing

High-speed automatic Raman spectroscopy
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To image the SWNTs on the fork-like substrate, a confocal Raman imaging system with a high-speed camera, a fast piezo stage and a high-resolution stepper has been installed. We use a high-speed line-scan approach to determine the number, position as well as properties of the SWNTs. This approach takes only hundreds of milliseconds for each trench and achieves a final imaging speed of over 3000 trenches per hour. The full imaging on the fork-like substrate is automated and controlled via a LabView interface.

Contact: Dr. Jian Zhang
Involved research groups:
Empa, Transport at Nanoscale Interfaces Laboratory
Identification of carbon nanotubes using deep learning
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We developed a high-throughput approach to rapidly identify suspended metallic (M-CNT) and semiconducting (S-CNT) carbon nanotubes by using high-speed Raman imaging and deep learning analysis. Even for Raman spectra with extremely low signal-to-noise ratios (SNR) of close to 1, we achieve a classification accuracy that exceeds 90%. Our approach can readily be extended to other types of nanomaterials and has the potential to be integrated into a production line to monitor the quality and properties of nanomaterials during fabrication.

Contact: Prof. Dr. Martin Jaggi
Involved research groups: Empa, Transport at Nanoscale Interfaces Laboratory; EPFL, Machine Learning and Optimization Laboratory

NanoAssembler: Fully Automated Carbon Nanotube Integration
The assembly of ultraclean carbon nanotube devices has been a tedious and slow process for a long time. During this project, our team developed a machine, which has speeded up and simplified this task significantly. It transfers fully automatically carbon nanotubes from a growth into a device chip, one by one . With a rate of over 300 transfers/hour it already exceeds the previously reported rates by a factor of 300, enabling wafer level production. In the video, a microscopic footage of a transfer motion is shown alongside the machine.

Contact: Dr. Natanael Lanz
Involved research groups: ETH Zurich, Institute of Machine Tools and Manufacturing; Empa, Transport at Nanoscale Interfaces Laboratory
PUBLICATIONS
2021
Jung, S., R. Hauert, M. Haluska, C. Roman and C. Hierold (2021). Understanding and improving carbon nanotube-electrode contact in bottom-contacted nanotube gas sensors. Sensors and Actuators B: Chemical 331: 129406.
2020
Zhang, J., M. L. Perrin, L. Barba, J. Overbeck, S. Jung, B. Grassy, A. Agal, R. Muff, R. Brönnimann, M. Haluska, C. Roman, C. Hierold, M. Jaggi and M. Calame (2022). High-speed identification of suspended carbon nanotubes using Raman spectroscopy and deep learning. Microsystems & Nanoengineering 8(1): 19.
2019
Franceschi, J.-Y., A. Dieuleveut and M. Jaggi (2019). Unsupervised Scalable Representation Learning for Multivariate Time Series. NeurIPS.
TEAM AND PARTNERS
Project Consortium
Prof. Dr. Michel Calame
Transport at Nanoscale Interfaces Laboratory (TNI), Empa

Dr. Cosmin Roman
Micro and Nanosystems (MNS), ETH Zürich

Prof. Dr. Martin Jaggi
Machine Learning and Optimization Laboratory (MLO), EPFL

Prof. Dr. Konrad Wegener
Institute of Machine Tools and Manufacturing (IWF), ETH Zürich
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Involved and Supporting Industry Partners
➜ Iminia Technologies SA
➜ IST AG
➜ Sensiron AG
➜ WITec GmbH
Leading Principal Investigator
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Prof. Dr. Michel Calame
Transport at Nanoscale Interfaces Laboratory (TNI),
Empa

An initiative of the ETH Board
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Participating Institutions of the ETH Domain
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Disclaimer
Imprint
© COPYRIGHT 2021. ALL RIGHTS RESERVED.
  • Home
  • Focus Areas
    • Focus Areas 2017-2020 >
      • Precision Free-Form Manufacturing
      • Printed Electronics
      • Sustainable Digital Manufacturing and Design
      • Sensing Technologies
      • Intelligent Systems and Advanced Automation
    • Focus Areas 2021-2024 >
      • Manufacturing Technologies
      • Functionality Integration
      • Sensing Technologies
      • Intelligent Systems and Advanced Automation
  • Projects
    • Projects Initial Program 2017-2020 >
      • Ceramic X.0
      • FUORCLAM
      • Powder Focusing
      • PREAMPA
      • FOXIP
      • CFRP-AM
      • SD4D
    • Projects Expansion Program 2017-2020 >
      • D-SENSE
      • MOCONT
      • Nano Assembly
      • SOL4BAT
    • Projects Program 2021-2024 >
      • AMYS
      • ClosedLoop-LM
      • DiPrintProtect
      • MANUFHAPTICS
      • Microfluidics
      • Multi-Mat
      • SCALAR
      • SMARTAM
  • Events
    • Annual Meetings >
      • Annual Review Meeting 2022
      • Annual Review Meeting 2021
      • Annual Review Meeting 2020
      • Annual Review Meeting 2019
      • Annual Review Meeting 2018
    • Industry Workshops >
      • Sensors
    • Other Events >
      • CERAMIC X.0 Workshop
      • Workshop 13 July 2020
      • Launch Event 13 Nov 2017
      • Workshop 6 July 2017
      • Workshop 17 Oct 2016
    • SAMCE
  • About
    • Steering Committee
    • Participating ETH Institutions
    • Calls & Selection >
      • Initial Program 2017-2020
      • Expansion Program 2017-2020
      • Continuation Program 2021-2024
  • Contact