Project description
Harnessing plasmons for photonic neuromorphic computing
The EIC-funded NEHO project plans to develop photonic computing technology to process data in a fast and energy-efficient way. The envisioned technology will incorporate hardware inspired by the structure and function of the human brain, demonstrating the ability to learn, adapt and evolve. At the core of NEHO’s vision is to leverage plasmonic properties in doped semiconductors. By controlling plasmonic effects, researchers will enhance optical nonlinearities, which are deemed central to realising artificial neurons.
Objective
NEHO will develop a novel photonic integrated circuit platform that enables ultrafast and low-energy consumption neuromorphic information processes by means of a newly developed nonlinear photon-plasmon semiconductor technology at mid-infrared wavelengths (8-12 μm). NEHO vision will be achieved by unconventional use of semiconductors to optimize and control plasmonic effects that will provide the optical nonlinearity required to implement the functionalities of an artificial neuron. NEHO's optical neuron will be the building block for the realization of ultrafast optical neural networks. We will combine the flexibility of field-effect devices realized on semiconductors with the nanoscale nature of plasmonic processes so to enable the reconfigurability of the nonlinear optical coefficient at each node of the network, simply obtained by controlling DC electric potential levels. At the heart of NEHO is the idea of exploiting the rich electron dynamics of semiconductors. Doped semiconductors undergo an interesting transition from the size-quantization regime to the classical regime of plasmon oscillations. This transition region can exhibit strong nonlocal and nonlinear optical response due to a large variety of electron-electron interactions. The decrease in electron density induced on the semiconductor surface by an external bias can be used to modulate the nonlinear response strength. This unprecedented feature will be used to leverage the hardware implementation of a neural network into the development of new machine learning optimization techniques, including the optimization of the nonlinear activation function to different tasks. This extra degree of freedom will offer tremendous benefits for a large variety of machine learning applications.
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HORIZON-EIC - HORIZON EIC GrantsCoordinator
16163 Genova
Italy