Li Ge, Xie Donggang, Zhang Qinghua, Zhang Mingzhen, Liu Zhuohui, Wang Zheng, Xie Jiahui, Guo Erjia, He Meng, Wang Can, Gu Lin, Yang Guozhen, Jin Kuijuan, Ge Chen
Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China.
School of Physical Sciences, University of Chinese Academy of Science, Beijing, China.
Nat Commun. 2025 Jan 2;16(1):57. doi: 10.1038/s41467-024-55412-6.
Ultraviolet (UV) detection is extensively used in a variety of applications. However, the storage and processing of information after detection require multiple components, resulting in increased energy consumption and data transmission latency. In this paper, a reconfigurable UV photodetector based on CeO/SrTiO heterostructures is demonstrated with in-sensor computing capabilities achieved through interface engineering. We show that the non-volatile storage capability of the device could be significantly improved by the introduction of an oxygen reservoir. A photodetector array operated as a single-layer neural network was constructed, in which edge detection and pattern recognition were realized without the need for external memory and computing units. The location and classification of corona discharges in real-world environments were also simulated and achieved an accuracy of 100%. The approach proposed here offers promising avenues and material options for creating non-volatile smart photodetectors.
紫外线（UV）检测在各种应用中被广泛使用。然而，检测后的信息存储和处理需要多个组件，这导致能耗增加和数据传输延迟。本文展示了一种基于CeO/SrTiO异质结构的可重构紫外光电探测器，通过界面工程实现了传感器内计算能力。我们表明，通过引入氧储存器可以显著提高器件的非易失性存储能力。构建了一个作为单层神经网络运行的光电探测器阵列，其中无需外部存储器和计算单元即可实现边缘检测和模式识别。还模拟了实际环境中电晕放电的位置和分类，准确率达到100%。这里提出的方法为创建非易失性智能光电探测器提供了有前景的途径和材料选择。
