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Machine Learning Models for a Novel Optical Memory Approach
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DOI：10.1021/acsomega.4c09541.png)
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摘要
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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 F1-score of 0.81 across nine distinct classes.
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IF:
4.3
论文数:
3.4W
被引数:
9.8W
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Ultra-compact III-V-on-Si photonic crystal memory for flip-flop operation at 5 Gb/s.png)
OPTICS EXPRESSOAAI
OPTICS EXPRESSIF3.3
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