Materials Science and Technology (ER-C-2)

The activities at ER-C-2 aim at providing an atomic-level understanding of structures, properties and fundamental mechanisms in structural, functional and electronic materials. A specific focus is placed on systems for energy harvesting, energy conversion and energy storage. Further fields of application include high-strength steels, lightweight materials for transportation, functional interfaces in composites and biomaterials and their technological counterparts.

Head: Prof. Dr. Joachim Mayer


Materials Science

We investigate the structures and properties of all relevant classes of materials, with a special emphasis on materials for renewable energy and future information technology.


Li-ion batteries

We investigate materials for Li-ion batteries on all length scales and help to optimise their properties with respect to charge density, ageing and reliability.


Memristive devices

New paradigms in computing involve filamentary switching in oxide materials for memristive devices and non-von-Neumann architectures such as neuromorphic computing.


Materials for hydrogen technology

We study membrane materials for power-to-X technologies in order to elucidate their atomic structures, defects and transport properties.


Structural materials

We investigate novel high strength and light weight materials for transportation and mobility.


Quantitative Modelling

We develop techniques to improve the understanding of material structures imaged with electrons allowing the determination of material properties on the atomic scale.

Spectroscopy ERC-2


We study materials and devices with micro-Raman and photoluminescence spectroscopy and correlate their optoelectronic properties with their structural and functional characteristics.

News and Events

FeRAM: Polarisationsordnungen in Hafniumoxid

Towards the Further Miniaturization of FeRAM

Jülich / Aachen, 2 February 2021. Ferroelectric Random Access Memory, or FeRAM for short, offers both working memory and data storage in one. This saves on the time and energy needed by conventional computers to transport data between the two units. In addition, the saved data is retained even without a power supply. The write performance and service life of these components are already outstanding and the first FeRAMs are already in use, for example in chip cards or RFID tags. However, comparatively small amounts of data can as yet be stored on a FeRAM, as the space required for storing individual bits is too large. The latest studies using electron microscopes by scientists at Forschungszentrum Jülich and RWTH Aachen University now indicate a way to reduce the size of FeRAM bits by more than a factor of 100.


PGI Colloquium: Prof. Dr. Emre Neftci, Forschungszentrum Jülich (PGI-15), Germany; previously Univ. of California, USA (Online Event)

The potential of machine learning to advance artificial intelligence is driving a quest to build dedicated systems that accelerate neural networks under real-world conditions. A natural approach to this is to take inspiration from neuroscience and build neuromorphic systems that emulate the biological processes of the brain using digital, mixed-signal and emerging nano-technologies.


ER-C for External Users

Access to the ER-C’s facilities is provided on the basis of peer-reviewed applications. Please contact our User Office for further information.

Gemeinschaftslabor für Elektronenmikroskopie

Central Facility for Electron Microscopy (GFE)

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