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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.05701 |
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| _version_ | 1866929306015367168 |
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| author | Kumar, Dayanand Li, Hanrui Singh, Amit Rajbhar, Manoj Kumar Syed, Abdul Momin Lee, Hoonkyung El-Atab, Nazek |
| author_facet | Kumar, Dayanand Li, Hanrui Singh, Amit Rajbhar, Manoj Kumar Syed, Abdul Momin Lee, Hoonkyung El-Atab, Nazek |
| contents | Photoresponsivity studies of wide-bandgap oxide-based devices have emerged as a vibrant and popular research area. Researchers have explored various material systems in their quest to develop devices capable of responding to illumination. In this study, we engineered a mature wide bandgap oxide-based bilayer heterostructure synaptic memristor to emulate the human brain for applications in neuromorphic computing and photograph sensing. The device exhibits advanced electric and electro-photonic synaptic functions, such as long-term potentiation (LTP), long-term depression (LTD), and paired pulse facilitation (PPF), by applying successive electric and photonic pulses. Moreover, the device exhibits exceptional electrical SET and photonic RESET endurance, maintaining its stability for a minimum of 1200 cycles without any degradation. Density functional theory calculations of the band structures provide insights into the conduction mechanism of the device. Based on this memristor array, we developed an autoencoder and convolutional neural network for noise reduction and image recognition tasks, which achieves a peak signal-to-noise ratio of 562 and high accuracy of 84.23%, while consuming lower energy by four orders of magnitude compared with the Tesla P40 GPU. This groundbreaking research not only opens doors for the integration of our device into image processing but also represents a significant advancement in the realm of in-memory computing and photograph sensing features in a single cell. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05701 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Negative Photo Conductivity Triggered with Visible Light in Wide Bandgap Oxide-Based Optoelectronic Crossbar Memristive Array for Photograph Sensing and Neuromorphic Computing Applications Kumar, Dayanand Li, Hanrui Singh, Amit Rajbhar, Manoj Kumar Syed, Abdul Momin Lee, Hoonkyung El-Atab, Nazek Applied Physics Photoresponsivity studies of wide-bandgap oxide-based devices have emerged as a vibrant and popular research area. Researchers have explored various material systems in their quest to develop devices capable of responding to illumination. In this study, we engineered a mature wide bandgap oxide-based bilayer heterostructure synaptic memristor to emulate the human brain for applications in neuromorphic computing and photograph sensing. The device exhibits advanced electric and electro-photonic synaptic functions, such as long-term potentiation (LTP), long-term depression (LTD), and paired pulse facilitation (PPF), by applying successive electric and photonic pulses. Moreover, the device exhibits exceptional electrical SET and photonic RESET endurance, maintaining its stability for a minimum of 1200 cycles without any degradation. Density functional theory calculations of the band structures provide insights into the conduction mechanism of the device. Based on this memristor array, we developed an autoencoder and convolutional neural network for noise reduction and image recognition tasks, which achieves a peak signal-to-noise ratio of 562 and high accuracy of 84.23%, while consuming lower energy by four orders of magnitude compared with the Tesla P40 GPU. This groundbreaking research not only opens doors for the integration of our device into image processing but also represents a significant advancement in the realm of in-memory computing and photograph sensing features in a single cell. |
| title | Negative Photo Conductivity Triggered with Visible Light in Wide Bandgap Oxide-Based Optoelectronic Crossbar Memristive Array for Photograph Sensing and Neuromorphic Computing Applications |
| topic | Applied Physics |
| url | https://arxiv.org/abs/2404.05701 |