Current Projects

Are you passionate about the nanoscale and its gateway, the AFM? Would you like to make use of your microfabrication skills to work on cutting-edge MEMS technology? Are you interested in observing biology at its smallest scale? Or do you want to push the imaging speeds of AFM through innovative control?

If any of those apply to you, then LBNI is the lab for your semester or master project!

Development of AFM cantilevers

Semester / Master project


Two primary research goals in AFM are increasing the imaging speed and improving the ease of use to expand the potential range of applications. To address these objectives, we have started developing polymer based [1], self-sensing cantilevers [2] to improve the mechanical bandwidth of the cantilevers and be able to use a simple electrical readout to detect the deflection of the cantilever. These self-sensing devices are a promising replacement for the current state of the art cantilevers that are used with a complex laser based optical readout. You will find more information about our devices here.


While we are able to fabricate such cantilevers with high yield, we are now looking to improve them and adapt them for specific AFM applications. To help us do so, we are looking for highly motivated students who want to learn multidisciplinary skills. In joining us, you will be collaborating with a PhD student and you will get to know all about the fabrication of MEMS devices the EPFL cleanrooms, the CMi. In addition you will learn how to perform self-sensing AFM imaging. A successful completion will be subject to publication.

Interested? The different research axes are listed below!

Proof of concept fabrication of silicon tips.

Our current cantilevers have no tips and we are forced to add them one by one manually, which is very time consuming. To address this problem, you will be charged with developing a process to add tips onto our cantilevers directly during their fabrication.

Piezoresponse force microscopy (PFM) image obtained with a conductive tip.

In various AFM techniques, conductive tips are used to probe the surface. Since our cantilevers have embedded electronics, we experience cross-talk effects between them and the tip-connection. You will have to modify the process flow such as to add a grounded metal connection between the two signal lines, then use the fabricated cantilevers for electrical measurements in AFM.

Trilayer cantilever with the sensor element in red.

We currently use the piezoresistive effect in poly-silicon to sense the deflection of our cantilevers. This material has a gauge factor of about 30. There are other materials, for instance single crystal silicon, that exhibit much higher gauge factors. You will be charged to modify the current process flow such as to replace the poly-silicon with single crystal silicon sensors.

What you will learn

  • Cleanroom fabrication skills like photolithography, dry/wet etching, thin film deposition
  • Creative thinking to find ways of manufacturing complex devices with standard microfabrication techniques
  • Layout design for photolithography
  • AFM imaging

Desired skills

  • Fluency in English
  • Autonomy
  • Enjoys learning and facing new challenges
  • Theoretical knowledge of microfabrication techniques (ex: fundamentals of microfabrication, materials and technology of microfabrication, MEMS practicals)


[1] Adams, Jonathan D., et al. “Harnessing the damping properties of materials for high-speed atomic force microscopy.” Nature nanotechnology (2015).

[2] Fantner, G. E., Adams, J. D. & HOSSEINI, N. Multilayer mems cantilevers. (2018).


At LBNI, we develop instruments at the forefront of nanoscale biology and one of our key strengths is instrumentation for atomic force microscopy (AFM). We develop high-speed AFM to push the current limits of imaging speeds. We believe that open hardware is the way forward to share the instruments and technologies that we develop, to the wider scientific community.


Atomic force microscopy (AFM) can take high-resolution images of nanoscale objects in various environments. Since conventional AFM imaging is too slow to capture dynamic processes of, for example, behavior and interaction of biomolecules, high speed AFM (HS-AFM) has been developed by optimizing the software and hardware in the instrument for fast scanning.

AFM images often require some post-processing in an open source software called Gwyddion. Data obtained from AFM imaging software Nanoscope can be directly imported, processed to flatten, remove lines and scars, and define the zero-level and contrast, then exported as a common image format such as jpeg. However, our existing software packages were not designed to handle the volume of data produced during HS AFM imaging, where we obtain hundreds of images per experimental run.

DNA tripod constructs assemble by blunt‐end stacking on mica into a mostly hexagonal lattice with pentagonal and heptagonal defects.


In high speed AFM imaging, we obtain a large quantity of images, which would take a significant amount of time to process with Gwyddion one by one. We are therefore looking for an interested Masters or semester project student to develop more efficient or automated AFM image processing for converting a series of HS-AFM images from the same experiment to a video format.

Project Task

Your task will be to design and test and a plugin for Gwyddion to efficiently process HS AFM data into videos.

What you will learn

  • AFM imaging
  • AFM image processing
  • Interface with open source AFM image processing software

Desired skills

  • Fluency in English
  • Coding basics (Python)
  • Ability and willingness to pick up new skills independently


Large-scale analysis of high-speed atomic force microscopy data sets using adaptive image processing

B. W. Erickson; S. Coquoz; J. D. Adams; D. J. Burns; G. E. Fantner 

Beilstein Journal of Nanotechnology. 2012. Vol. 3, p. 747-758. DOI : 10.3762/bjnano.3.84.

Large‐Range HS‐AFM Imaging of DNA Self‐Assembly through In Situ Data‐Driven Control

A. P. Nievergelt; C. M. Kammer; C. Brillard; E. E. Kurisinkal; M. Bastings et al. 

Small Methods. 2019. Vol. 3, p. 1900031. DOI : 10.1002/smtd.201900031.