Reversible training of waveguide-based AND/OR gates for optically driven artificial neural networks using photochromic molecules

Journal of Physics D Applied Physics, vol. 55, pp. 044002

Abstract

Artificial neural networks (ANNs) are inspired by the biological nervous system. The high performance of such ANNs is achieved through the dynamic change of the synaptic weights by applying self-optimizing learning algorithms. Despite the simple operations for each single element in an ANN, a network with a huge number of simulated elements consumes lots of computing capacity using von Neumann computer architectures. To overcome this issue, neuromorphic devices facilitate the design of hardware ANNs that emulate the synaptic functions. Here we demonstrate the viability of such an approach using photonic waveguides in combination with a photochromic diarylethene (DAE) molecule. By positioning and irradiating DAE molecules on single waveguides, we modulate the intensity and thereby emulate the plasticity of the synaptic weights. To run the photonic device as an ANN we firstly characterize the modulation range and encode a learning procedure accordingly. As the proof of concept, we operate a y-shaped waveguide performing basic AND/OR logic gate functions, with the capability to switch between these two gate functions by using specific training sets.

Authors 7

1. Seon‐Young Rhim

   Humboldt-Universität zu Berlin

   Affiliation as printed

   Institut für Physik, Institut für Chemie & IRIS Adlershof, Humboldt-Universität zu Berlin, Zum Großen Windkanal 2, 12489 Berlin, Germany

2. Giovanni Ligorio

   Humboldt-Universität zu Berlin

   Affiliation as printed

   Institut für Physik, Institut für Chemie & IRIS Adlershof, Humboldt-Universität zu Berlin, Zum Großen Windkanal 2, 12489 Berlin, Germany

3. Felix Hermerschmidt

   Humboldt-Universität zu Berlin

   Affiliation as printed

   Institut für Physik, Institut für Chemie & IRIS Adlershof, Humboldt-Universität zu Berlin, Zum Großen Windkanal 2, 12489 Berlin, Germany

4. Michael Pätzel

   Humboldt-Universität zu Berlin

   Affiliation as printed

   Institut für Chemie & IRIS Adlershof, Humboldt-Universität zu Berlin, Brook-Taylor-Straße 2, 12489 Berlin, Germany

5. Martin Herder

   Humboldt-Universität zu Berlin

   Affiliation as printed

   Institut für Chemie & IRIS Adlershof, Humboldt-Universität zu Berlin, Brook-Taylor-Straße 2, 12489 Berlin, Germany

6. Stefan Hecht Aachen DWI ‐ Leibniz‐Institut für Interaktive Materialien e.V Institut für Technische und Makromolekulare Chemie

   Humboldt-Universität zu Berlin · DWI – Leibniz Institute for Interactive Materials · RWTH Aachen University

   Affiliation as printed

   DWI—Leibniz Institut für interaktive Materialien e.V., RWTH Aachen University, Forckenbeckstraße 50, 52074 Aachen, Germany

   Institut für Chemie & IRIS Adlershof, Humboldt-Universität zu Berlin, Brook-Taylor-Straße 2, 12489 Berlin, Germany

   Institut für Technische und Makromolekulare Chemie, RWTH Aachen University, Worringerweg 2, 52074 Aachen, Germany

7. Emil J. W. List corresponding

   Helmholtz-Zentrum Berlin für Materialien und Energie · Humboldt-Universität zu Berlin

   Affiliation as printed

   Helmholtz-Zentrum für Materialien und Energie GmbH, Hahn-Meitner-Platz 1, 14109 Berlin, Germany

   Institut für Physik, Institut für Chemie & IRIS Adlershof, Humboldt-Universität zu Berlin, Zum Großen Windkanal 2, 12489 Berlin, Germany