| Autor: |
Minjoo Kim, Beomju Kim, Yelim Kim, Lia Saptini Handriani, Suhee Jang, Dae Yeop Jeong, Sung Ik Yang, Won Il Park |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Photonics Journal, Vol 16, Iss 2, Pp 1-8 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1943-0655 |
| DOI: |
10.1109/JPHOT.2024.3361930 |
| Popis: |
We developed an optical neural network (ONN) for efficient processing and recognition of 2-dimensional (2D) images, employing a conventional liquid crystal display panel as optical neurons and synapses. This configuration allowed for optical signal outputs proportional to matrix-vector multiplication for 2D image inputs. However, our experimental results revealed a 26.6% decrease in the optical classification accuracy, despite utilizing digitally pre-trained parameters with 100% accuracy for 500 handwritten digits. This decline can be attributed to system imperfections associated with non-ideal functions of optical components and optical alignment. Rather than pursuing an elusive, imperfection-free ONN or attempting to calibrate these defects individually, we addressed these challenges by introducing a self-correction mechanism that utilizes a machine learning algorithm. This approach effectively restored the recognition accuracy and minimized loss of our ONN to levels comparable to the digitally pre-trained model. This study underscores the potential of constructing defect-tolerant hardware in ONNs through the application of machine learning techniques. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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