Optical Neural Networks (ONNs)
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광학 신경망
Optoelectronic Materials & Devices for Optical Neural Networks (ONNs)
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Optoelectronic materials, particularly 1D and 2D nanomaterials, play a crucial role in the development of Optical Neural Networks (ONNs), offering high-speed, low-power, and parallel computation capabilities. By leveraging advanced nanomaterials, ONN systems can achieve enhanced optical signal processing and efficient deep learning operations. The integration of ONNs with machine learning-based self-correction mechanisms further improves system accuracy and robustness. 

Additionally, ONNs enable optical convolution operations for high-speed color image recognition, providing significant advantages over conventional electronic neural networks. Furthermore, ONN-based optical computing facilitates efficient image processing and generation using autoencoders, paving the way for applications in real-time image analysis, pattern recognition, and next-generation artificial intelligence systems. The development of ONN systems based on heterostructures of 1D and 2D nanomaterials opens new possibilities for highly efficient and scalable optical computing architectures.
The primary research topics in this field include
  1. Implementation of Optical Neural Network (ONN) systems and machine learning-based self-correction processes.
  2. Optical convolution operations using ONNs for color image recognition.
  3. ONN-based optical computing for image processing and generation using autoencoders.
  4. Development of ONN systems utilizing heterostructures of 1D and 2D nanomaterials.
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