Organic neuromorphic devices
Artistic representation of our organic artificial synapse.

Small array of Neuromorphic Devices

Brain-inspired computing based on existing CMOS technology has revolutionised the fields of data analytics and pattern recognition but has yet to achieve the interconnectivity and energy efficiency of the brain. Specifically, at the moment many transistors are necessary to simulate one synapse and neural networks are mostly simulated on large supercomputer clusters, consuming a lot of energy. Alternatively, research has been focused on memristorstwo-terminal devices that act as tuneable resistors and useful as non-volatile elements in efficient hardware-based neural networks. However, these memristors require relatively high currents or voltages for switching a limited amount of stochastic states. Our research has been focused on developing a flexible low-energy organic artificial synapse that has many non-volatile conductance states, thereby outperforming any memristor to date and opening up possibilities in developing a completely new field of neuromorphic computing.

The use of organic materials opens up possibilities in flexible, low-cost and low-energy neuromorphic computing. On top of that, the easy acces to a large linear and symmetric conductance range of these materials and devices offer opportunities for efficient implementation of hardware-based neural networks such as vector-matrix multiplication.

At the moment our research consists of optimising the material systems, stability and processability. At the same time we aim to develop a large (crossbar) array of organic neuromorphic devices to perform some basic neural network calculations. Finally, one of the specific applications could be found in dedicated smart brain-inspired point-of-care devices that can be trained to classify and recognise diseases or cells.

Organic materials allow for flexible devices
Organic and flexible electronics interfaced with biology
Organic bioelectronics allows us to interface devices with biological environments

Our collaboration with the Salleo lab also extends further into the fast developing field of organic electronics. Specifically, organic electrochemical transistors (OECTs) are being investigated. Our group is still interested in the broad field of electrochemical transistors for specific applications such as sensors or implants. A collaboration with prof. Malliaras at Cambridge University aims to develop specific trainable brain-machine interfaces based on these OECTs.

Our research collaboration with Santoro Lab at IIT in Naples involves the investigation and enhancement of biological cells with (organic) substrate and interfaces. Using a specially developed FIB/SEM method we are able to visualize the cell material interaction in high resolution. With nanostructures, such as nanopillars or grooves we can enhance the attachment of cells even further. We can link the electrochemical response of our devices to this attachment to the highly interesting electrical properties of the cleft between the cell and substrate. Furthermore, tuneable materials can be used to locally adapt the connection strength with cells or tissue, enabling hybrid biological memory devices.

Smart robotics

Together with Dr. Paschalis Gkoupidenis at the Max Planck Institute in Mainz, our group works on smart and autonomous robotics.

Using low-voltage and low-power organic devices, robotics can be fully autonomous and operate without external control. We train simple robots to find their way and classify odours.

Neuromorphic devices for Smart Robotics


Leave a Reply

Your email address will not be published. Required fields are marked *