The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Research

Variation of signal intensity in photonic circuit. Simulation by David Winge.

The long term vision of this project is a novel on-chip hybrid nanostructure platform for energy-efficient, fast artificial neural networks and integrated sensor arrays. It is based on (i) neural circuit architectures found in insects (ii) replacing physical interconnects by light (iii) using novel nanoscale components and molecular dyes to control and interpret signals with extreme energy efficiency.

How the insect path integration leads to a nanophotonic chip device. Illustration by David Winge.
Illustration of an insect's path integration and the computer models derived from this. Illustration by David Winge

Understanding of the neuro-architecture of key areas in the insect brain and its attached sensory systems will be used to create III-V nanowire and molecular dye-based network systems that mimic neural computations underlying specific behaviours (in particular, navigation). Insights into how the sensory array of the insect eye couples to navigation control circuits will drive the development of coupled nanostructure sensor arrays and navigation systems.

We will demonstrate and explore three main functionalities: connectivity, memory, and sensing; as well as concurrently develop the upscaling/commercial aspects:

  • Objective 1: Demonstrate superior connectivity using overlapping light signals in a nanoscale system. To use light for connectivity we apply a broadcasting concept sensitizing the neural nodes to specific light signals and by sub-wavelength light manipulation of emission patterns using III-V nanowire-based components as well as molecular dyes.
    Molecular Dye. Photo by Bo W. Laursen.
    Ultra-bright molecular dye. Photo: Bo W. Laursen
  • Objective 2: Explore neuromorphic memory functionalities from nanoelectronics and molecular dyes. Based on neurobiology studies of the insect working memory we will explore how several different memory concepts can be implemented using III-V nanowires and molecular dyes.
  • Objective 3: Integrate optical sensor systems and information processing. The same nanostructures used for computing will be used for optical sensing. A neural network unit will extract global orientation information from polarised skylight and time of day.
  • Objective 4: Show upscaling, on-chip assembly and market potential. Working on scalability, energy efficiency and potential for optimization, we will show the generality of the approach and the next steps towards large-scale commercialization.  
Prerequisites for this research: understanding of insect brain, ultrabright fluorescence and nanowires platforms. Collage.
Components in this research: (1) understanding of insect brain organization, here represented by a 3D reconstruction of all neuronal branches of one bumblebee nodulus at medium resolution (24nm), demonstrating ability to obtain connectomics data. Illustration by Stanley Heinze. (2) Ultrabright fluorescent dyes with long lifetime (graphics from J. Am. Chem. Soc. 2021, 143, 3, 1377–1385) and (3) nanowire arrays that can form the basis for nanophotonic circuit development (graphics from Nano Lett. 2021, 21, 17, 7347–735 ).

Our approach to develop nanophotonic computational devices inspired by neural circuits in insect brains combines four advanced lines of research:

  1. The rapidly developing understanding of insect neurological and sensory systems
  2. An extremely advanced III-V semiconductor nanowire platform.
  3. Circuit technology developed for quantum computing.
  4. Optically efficient, stable molecular dyes.

The combined circuits can be directly placed on the Silicon technology platform

Detailed electrical and optical modelling of an initial nanophotonic implementation of a decision-making circuit from the insect brain shows three main advantages of our approach:

  1. Weighted component interconnectivity can be achieved by controlling light emission patterns and physical placement of individual neural nodes of the system. This bypass the huge amounts of physical connections needed in standard implementations of neural circuits.
  2. The energy expenditure can be kept significantly below present computing systems by using nano-optoelectronic components.
  3. The underlying network models are extremely robust to noise and cross-talk, making implementation feasible with weak signals and non-ideal components/connections.