Year: 2025
Type: article
Abstract: Limitations of electronics have stimulated the search for novel unconventional computing platforms that enable energy-efficient and ultra-fast information processing. Among various systems, exciton-polaritons stand out as promising candidates for the realization of optical neuromorphic devices. This is due to their unique hybrid light-matter properties, resulting in strong optical nonlinearity and excellent transport capabilities. However, previous implementations of polariton neural networks are restricted to cryogenic temperatures, limiting their practical applications. In this work, using non-equilibrium Bose-Einstein condensation in a monocrystalline perovskite waveguide, the first room-temperature exciton-polariton neural network is demonstrated. Its performance is verified in various machine learning tasks, including binary classification, and object detection. The result is a crucial milestone in the development of practical applications of polariton neural networks and provides new perspectives for optical computing accelerators based on perovskites.
Source: Advanced Materials
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $6,070
OpenAlex ID: W4413833643