Lilan Zou, Junru An, Haonan Xu, Guizhen Wang, Shiwei Lin
{"title":"一种具有双功能光电探测器和神经形态视觉光学突触行为的超薄光电忆阻器","authors":"Lilan Zou, Junru An, Haonan Xu, Guizhen Wang, Shiwei Lin","doi":"10.1002/aelm.202400992","DOIUrl":null,"url":null,"abstract":"Integrating multiple functions within a neuromorphic device is essential for simplifying circuit design in compact artificial vision applications. At the same time, there is a constant push to reduce the size of devices to improve integration. Nevertheless, decreasing the thickness of the active layer compromises photoelectric performance, affecting stability, uniformity, endurance, and photosensitivity. An optoelectronic memristor featuring an ultrathin AlO<sub>x</sub>/TiO<sub>y</sub> periodic heterostructure is proposed. This design minimizes the active layer thickness without compromising optoelectronic properties and enables multifunctionality as a photodetector, electric synapse, and optical synapse in a single device. The periodic heterostructure is successfully prepared by atomic layer deposition with a thickness of only ≈12 nm. The device enables electric synaptic behaviors, which are essential for neuromorphic computing. Notably, the dual-functional photodetector and optical synapse facilitate the efficient acquisition and processing of visual information following specific application scenarios. It enables visual attention simulation for energy-efficient object detection. Finally, a complete visual system is demonstrated, encompassing sensing, front-end preprocessing, and back-end computing. Based on the proposed system, a six-layer convolutional neural network is built to recognize EMNIST patterns, and front-end preprocessing improves recognition accuracy from 64% to 78%.","PeriodicalId":110,"journal":{"name":"Advanced Electronic Materials","volume":"68 1","pages":""},"PeriodicalIF":5.3000,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Ultrathin Optoelectronic Memristor with Dual-Functional Photodetector and Optical Synapse Behaviors for Neuromorphic Vision\",\"authors\":\"Lilan Zou, Junru An, Haonan Xu, Guizhen Wang, Shiwei Lin\",\"doi\":\"10.1002/aelm.202400992\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Integrating multiple functions within a neuromorphic device is essential for simplifying circuit design in compact artificial vision applications. At the same time, there is a constant push to reduce the size of devices to improve integration. Nevertheless, decreasing the thickness of the active layer compromises photoelectric performance, affecting stability, uniformity, endurance, and photosensitivity. An optoelectronic memristor featuring an ultrathin AlO<sub>x</sub>/TiO<sub>y</sub> periodic heterostructure is proposed. This design minimizes the active layer thickness without compromising optoelectronic properties and enables multifunctionality as a photodetector, electric synapse, and optical synapse in a single device. The periodic heterostructure is successfully prepared by atomic layer deposition with a thickness of only ≈12 nm. The device enables electric synaptic behaviors, which are essential for neuromorphic computing. Notably, the dual-functional photodetector and optical synapse facilitate the efficient acquisition and processing of visual information following specific application scenarios. It enables visual attention simulation for energy-efficient object detection. Finally, a complete visual system is demonstrated, encompassing sensing, front-end preprocessing, and back-end computing. Based on the proposed system, a six-layer convolutional neural network is built to recognize EMNIST patterns, and front-end preprocessing improves recognition accuracy from 64% to 78%.\",\"PeriodicalId\":110,\"journal\":{\"name\":\"Advanced Electronic Materials\",\"volume\":\"68 1\",\"pages\":\"\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2025-05-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced Electronic Materials\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1002/aelm.202400992\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Electronic Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1002/aelm.202400992","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
An Ultrathin Optoelectronic Memristor with Dual-Functional Photodetector and Optical Synapse Behaviors for Neuromorphic Vision
Integrating multiple functions within a neuromorphic device is essential for simplifying circuit design in compact artificial vision applications. At the same time, there is a constant push to reduce the size of devices to improve integration. Nevertheless, decreasing the thickness of the active layer compromises photoelectric performance, affecting stability, uniformity, endurance, and photosensitivity. An optoelectronic memristor featuring an ultrathin AlOx/TiOy periodic heterostructure is proposed. This design minimizes the active layer thickness without compromising optoelectronic properties and enables multifunctionality as a photodetector, electric synapse, and optical synapse in a single device. The periodic heterostructure is successfully prepared by atomic layer deposition with a thickness of only ≈12 nm. The device enables electric synaptic behaviors, which are essential for neuromorphic computing. Notably, the dual-functional photodetector and optical synapse facilitate the efficient acquisition and processing of visual information following specific application scenarios. It enables visual attention simulation for energy-efficient object detection. Finally, a complete visual system is demonstrated, encompassing sensing, front-end preprocessing, and back-end computing. Based on the proposed system, a six-layer convolutional neural network is built to recognize EMNIST patterns, and front-end preprocessing improves recognition accuracy from 64% to 78%.
期刊介绍:
Advanced Electronic Materials is an interdisciplinary forum for peer-reviewed, high-quality, high-impact research in the fields of materials science, physics, and engineering of electronic and magnetic materials. It includes research on physics and physical properties of electronic and magnetic materials, spintronics, electronics, device physics and engineering, micro- and nano-electromechanical systems, and organic electronics, in addition to fundamental research.