On-chip reconfigurable diffractive optical neural network based on Sb2S3
Year: 2024
Type: article
Abstract: , a low-loss phase change material. The RDONN architecture is constructed using the two-dimensional electromagnetic propagation model and implements the classification task on the Iris dataset with both intensity modulation and phase modulation inputs. This demonstrates its feasibility, with classification accuracies reaching 95.0% and 98.3%, respectively. Our model enables reconfigurable manipulation of the weights in the on-chip diffractive optical neural network, which can be used in the design and fabrication of real chips. This advancement holds significant promise for future all-optical in situ learning systems.
Source: Optics Express
DOI: 10.1364/oe.545535
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,270
OpenAlex ID: W4405585666