Conventional CMOS vision systems suffer from severe power consumption and data latency due to the physical separation of sensing and processing units. While in-sensor computing architectures offer a promising solution by integrating processing units directly at the pixel level. However, existing neuromorphic devices are often constrained by unidirectional photocurrent and a reliance on external bias, hindering the simultaneous optimization of functional reconfigurability and energy efficiency. Furthermore, device schemes based on Schottky barriers or ferroelectric materials like PVDF often face challenges such as high programming voltages, low reliability, and incompatibility with standard CMOS processes, which significantly limits their scalability in high-performance vision systems.
To address these critical issues, a joint research team led by Prof. Xiaoxian Zhang and Prof. Yongsheng Wang from Beijing Jiaotong University, in collaboration with Prof. Yuchao Yang and Assistant Prof. Yaoyu Tao from Peking University, reported a reconfigurable photodiode based on an ambipolar semiconductor WSe2 channel, leveraging the mechanism of ferroelectric polarization-modulated polarity. By integrating a sub-20-nm HfxZr1-xO2 (HZO) ferroelectric layer with a split-gate architecture, the device achieves polarity-switchable photocurrent under zero external bias, featuring a sub-fJ programming energy, a switching speed of 50 μs, and weight retention exceeding 100 s.
The device supports in-situ visual preprocessing of incident optical signals, such as matrix-vector multiplication. When employed as a physical convolution kernel for simulated edge detection tasks, it achieves an exceptionally low normalized mean squared error of approximately 3.2 × 10-4 at the optimal weight state, producing edge maps nearly indistinguishable from ideal software predictions. This work establishes a CMOS-compatible device paradigm, providing a core building block for future reconfigurable, integrated sensing-memory-computing vision systems.
Other contributors include Jiarong Wang, Jinwen Zhuang, Yinglin Zhang, Chengzhi Zhang, Wenwen Wu, Prof. Dawei He from the Institute of Optoelectronic Technology at Beijing Jiaotong University, and Keqin Liu, Pek Jun Tiw, Xin Shan from the School of Integrated Circuits at Peking University.
This work has been supported by the National Key R&D Program of China (2023YFA1407200, 2023YFB4502200), Guangdong Provincial Key Laboratory of In-Memory Computing Chips (2024B1212020002), Shenzhen Science and Technology Program (JCYJ20241202125907011), Beijing Natural Science Foundation (L234026, L257010), National Natural Science Foundation of China (62374014, 92164302), and Financial Support for Outstanding scientific and technological innovation Talents Training Fund in Shenzhen. This work has been supported by the New Cornerstone Science Foundation.
DOI Link:
https://doi.org/10.26599/NR.2026.94908610
About the Author
Dr. Xiaoxian Zhang is a Professor at the Institute of Optoelectronic Technology, Beijing Jiaotong University. Her primary research focuses on micro/nano optoelectronic devices and nanoptoelectronics. Until now, she has published over 70 papers in premier journals such as Nature Communications, Nano Letters, and the Journal of the American Chemical Society (JACS).
About Nano Research
Nano Research is a peer-reviewed, open access, international and interdisciplinary research journal, sponsored by Tsinghua University and the Chinese Chemical Society, published by Tsinghua University Press on the platform SciOpen. It publishes original high-quality research and significant review articles on all aspects of nanoscience and nanotechnology, ranging from basic aspects of the science of nanoscale materials to practical applications of such materials. After 18 years of development, it has become one of the most influential academic journals in the nano field. Nano Research has published more than 1,000 papers every year from 2022, with its cumulative count surpassing 8,000 articles. In 2025 InCites Journal Citation Reports, its 2025 IF is 9.4 (8.3, 5 years), and it continues to be the Q1 area among the four subject classifications. Nano Research Award, established by Nano Research together with TUP and Springer Nature in 2013, and Nano Research Young Innovators (NR45) Awards, established by Nano Research in 2018, have become international academic awards with global influence.
