(2DoNeuron) 2D photonic memristive devices for neuromorphic applications

At a glance

Title 2D photonic memristive devices for neuromorphic applications
Reference 2022.15710.UTA
Scientific Area Nano Materials for New Markets
Funding (PT) 49 894 EUR
Funding (US) 100 000 USD
Leading Institutions Institute of Physics for Advanced Materials, Nanotechnology and Photonics (IFIMUP) / University of Porto, PT

The University of Texas at Austin, USA

Participating Institutions
Duration 12 months
Start date May 1, 2024
End date April 30, 2025
Keywords Memristor; 2D; Photonic; Neuromorphic

What is 2DoNeuron about?

2DoNeuron aims to develop electrical and optically controllable artificial neural networks based on photonic, low-power consumption and high integration density 2D-based memristors. This will allow to directly train optical memristors to classify standard handwritten digit images as a first proof-of principle case study.

Our vision is that the assembly of such optical adaptive switches, based on rich 2D materials properties, into novel architecture systems will provide a revolutionary paradigm for heavily distributed, ultrafast data processing with vast impact in neuromorphic computing.

By successfully accomplishing the fabrication of an optical neural network we will give a significant step to pave the way for real-time processing of information encoded by light patterns.

What critical challenges is 2DoNeuron addressing?

The pursuit for new device architectures and novel materials is essential for the next generation computing technology. Accordingly, memristors have attracted increasing attention due to their unique properties and great potential in neuromorphic computing.

Moreover, if light can be used as an extra control parameter in these memristors, it is attractive for remote controlled memory and artificial synapses. Therefore, one needs optically active materials meeting the demands of the active memristor layer, which makes novel two-dimensional (2D) materials promising for multifunctional optoelectronic computing and memory.

How will 2DoNeuron optimize the production of neuromorphic applications?

Our strategy will start with the fabrication of optimized 2D materials-based memristive devices, profiting from the extended know-how and experience of the partner groups on resistive switching behaviors and 2D-materials fabrication. We will use a combination of appropriate materials, growth conditions and lithography processes.

The memristive devices will display neuromorphic properties such as the extended Hebb rule and STDP. The operation parameters will be studied both under electrical and optical stimulation. This is a new field, with 2D-based memristors still in their infancy. To take full advantage of 2D materials, the fabrication should be significantly optimized for integration with optical systems, and it is very important to understand the underlying switching mechanisms.

How is 2DoNeuron contributing to nanomaterial research?

The proposed work focuses on the discovery and development of innovative nanomaterials, with a range of unique properties suitable for diverse applications, including medical diagnostics and therapy. The team will study never-before tested nanomaterials with the potential to be used in state-of-the-art treatment methods for lung cancer. The results developed can be transposed for the treatment of other types of cancer. This disease affects the population worldwide with an incidence of 18.1 million new cancer cases and 9.6 million cancer deaths per year, making early detection and more selective treatment strategies paramount to fighting this disease effectively.

Key Expected Outcomes

  • 2 International publications in high impact journals, one of them in open access.
  • 2 Conferences/seminars, inviting the collaborators from UT-Austin to show their work to the Portuguese research community.

Project Team

Catarina Dias

Junior Researcher at IFIMUP - University of Porto

Deji Akinwande

Professor at the University of Texas at Austin
  • Other team members in Portugal: João Ventura (Team Member; IFIMUP – University of Porto)

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