Year: 2024
Type: article
Abstract: High Resolution Image Download MS PowerPoint Slide In recent years, there has been growing interest in optical data processing, driven by the demand for high-speed and high-bandwidth data handling in data centers. One of the key milestones for enabling effective all-optical data processing systems is the development of efficient optical memory. Previously, we introduced a novel approach for establishing nonvolatile optical memory, based on the classification of scattering fields generated by gold nanoparticles. In this ongoing research, we apply advanced machine learning techniques to enhance the performance of the proposed nonvolatile memory element. By utilizing Random Forest and t-SNE algorithms, we successfully classified and analyzed the scattered images obtained from the optical memory device. The classification model presented in this study achieved an accuracy and average F 1-score of 0.81 across nine distinct classes.
Source: ACS Omega
Authors: Tal Raviv, Zeev Kalyuzhner, Zeev Zalevsky
Institution: Bar-Ilan University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,935
OpenAlex ID: W4405252192