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Peer-reviewedCC BY-NC-ND 4.0Npg Asia Materials

Emerging light-driven neuromorphic hardware for artificial intelligence

  • Panagiotis Bousoulas1
  • Spyros Orfanoudakis1,2
  • Charalampos Tsioustas1
  • Chrysi Panagopoulou1
  • Danai Spathi1
  • Leonidas Tsetseris1
  • Athanassios G. Kontos1,2
  • Thomas Stergiopoulos2
  • Dimitris Tsoukalas1
  1. 1Department of Physics, School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Iroon Polytechniou 9 Zographou, Athens, 15780 Greece
  2. 2Institute of Nanoscience and Nanotechnology, NCSR Demokritos, 15341 Athens, Greece
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Abstract

Optoelectronic neuromorphic systems have emerged as a promising hardware paradigm for next-generation artificial intelligence, combining the high bandwidth, parallelism, and wavelength selectivity of photonics with the adaptive plasticity of electronic materials. This mini-review provides a focused and critical overview of recent advances in material platforms, device architectures, and system-level implementations enabling light-driven neuromorphic computation. We comparatively analyze key photoresponsive materials—including halide perovskites, low-dimensional semiconductors, phase-change and oxide systems, and organic–inorganic hybrids—highlighting their underlying physical mechanisms such as photocarrier generation, charge trapping, ion migration, and excitonic effects. Particular emphasis is placed on device concepts, including optoelectronic synapses, neurons, and crossbar arrays, as well as their integration into in-sensor and hybrid photonic–electronic architectures for machine vision and real-time perception. A benchmarking analysis is presented to evaluate trade-offs in speed, energy consumption, retention, scalability, and stability across different material systems. Finally, we discuss key technological bottlenecks—including device variability, lack of standardized metrics, and integration challenges—and outline future research directions toward scalable, energy-efficient, and application-specific optoelectronic neuromorphic processors.

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Cite

BibTeXRIS
BibTeX
@article{bousoulas2026emerging,
  title = {{Emerging light-driven neuromorphic hardware for artificial intelligence}},
  author = {Panagiotis Bousoulas and Spyros Orfanoudakis and Charalampos Tsioustas and Chrysi Panagopoulou and Danai Spathi and Leonidas Tsetseris and Athanassios G. Kontos and Thomas Stergiopoulos and Dimitris Tsoukalas},
  journal = {Npg Asia Materials},
  year = {2026},
  month = aug,
  doi = {10.1038/s41427-026-00671-6},
  url = {https://doi.org/10.1038/s41427-026-00671-6},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Emerging light-driven neuromorphic hardware for artificial intelligence
AU  - Panagiotis Bousoulas
AU  - Spyros Orfanoudakis
AU  - Charalampos Tsioustas
AU  - Chrysi Panagopoulou
AU  - Danai Spathi
AU  - Leonidas Tsetseris
AU  - Athanassios G. Kontos
AU  - Thomas Stergiopoulos
AU  - Dimitris Tsoukalas
PY  - 2026
DA  - 2026/08/01
JO  - Npg Asia Materials
DO  - 10.1038/s41427-026-00671-6
UR  - https://doi.org/10.1038/s41427-026-00671-6
AB  - Optoelectronic neuromorphic systems have emerged as a promising hardware paradigm for next-generation artificial intelligence, combining the high bandwidth, parallelism, and wavelength selectivity of photonics with the adaptive plasticity of electronic materials. This mini-review provides a focused and critical overview of recent advances in material platforms, device architectures, and system-level implementations enabling light-driven neuromorphic computation. We comparatively analyze key photoresponsive materials—including halide perovskites, low-dimensional semiconductors, phase-change and oxide systems, and organic–inorganic hybrids—highlighting their underlying physical mechanisms such as photocarrier generation, charge trapping, ion migration, and excitonic effects. Particular emphasis is placed on device concepts, including optoelectronic synapses, neurons, and crossbar arrays, as well as their integration into in-sensor and hybrid photonic–electronic architectures for machine vision and real-time perception. A benchmarking analysis is presented to evaluate trade-offs in speed, energy consumption, retention, scalability, and stability across different material systems. Finally, we discuss key technological bottlenecks—including device variability, lack of standardized metrics, and integration challenges—and outline future research directions toward scalable, energy-efficient, and application-specific optoelectronic neuromorphic processors.
SN  - 1884-4057
ER  -