@article{Sheng2026, 
author = {Chenxu Sheng and Shuwen Shen and Laigui Hu and Xiaofei Yue and Shoaib Awan and Dacheng Xia and Jiao Wang and Zhi-Jun Qiu and Chunxiao Cong and Ran Liu},
title = {Unveiling the invisible: Polarization-sensitive ferroelectric photomemristors for enhanced image recognition},
year = {2026},
journal = {Nano Research},
volume = {19},
number = {1},
pages = {94908019},
keywords = {artificial neural network, rhenium selenide, diisopropylammonium bromide, polarization-sensitive ferroelectric photomemristor, low-contrast image recognition},
url = {https://www.sciopen.com/article/10.26599/NR.2025.94908019},
doi = {10.26599/NR.2025.94908019},
abstract = {Photoresponsive memristors (i.e., photomemristors) have been recently highly regarded to tackle data latency and energy consumption challenges in conventional Von Neumann architecture-based image recognition systems. However, their efficacy in recognizing low-contrast images is quite limited, and while preprocessing algorithms are usually employed to enhance these images, which naturally introduce delays that hinder real-time recognition in complex conditions. To address this challenge, here we present a self-driven polarization-sensitive ferroelectric photomemristor inspired by advanced biological systems. The proposed prototype device is engineered to extract image polarization information, enabling real-time and in-situ enhanced image recognition and classification capabilities. By combining the anisotropic optical feature of the two-dimensional material (ReSe2) and ferroelectric polarization of single-crystalline diisopropylammonium bromide (DIPAB) thin film, tunable and self-driven polarized responsiveness with intelligence was achieved. With remarkable optoelectronic synaptic characteristics of the fabricated device, a significant enhancement was demonstrated in recognition probability—averaging an impressive 85.9% for low-contrast scenarios, in contrast to the mere 47.5% exhibited by traditional photomemristors. This holds substantial implications for the detection and recognition of subtle information in diverse scenes such as autonomous driving, medical imaging, and astronomical observation.}
}