@article{Wang2026, 
author = {Jiarong Wang and Keqin Liu and Jingwen Zhuang and Yinglin Zhang and Pek Jun Tiw and Chengzhi Zhang and Wenwen Wu and Xin Shan and Dawei He and Yongsheng Wang and Yaoyu Tao and Yuchao Yang and Xiaoxian Zhang},
title = {Sub-fJ programming in ferroelectric reconfigurable homojunctions for photovoltaic in-sensor computing},
year = {2026},
journal = {Nano Research},
volume = {19},
number = {6},
pages = {94908610},
keywords = {ferroelectricity, WSe2, reconfigurable photodiode, in-sensor computing, HfxZr1−xO2 (HZO)},
url = {https://www.sciopen.com/article/10.26599/NR.2026.94908610},
doi = {10.26599/NR.2026.94908610},
abstract = {Conventional vision systems encounter data bottlenecks and high-power consumption in emerging applications due to the separation of sensing and computation. In-sensor computing architectures address this limitation by integrating reconfigurable, self-powered photodiodes at the pixel level to enable in-situ visual preprocessing. However, existing devices are constrained by high programming energy, poor weight retention, or complementary metal-oxide-semiconductor (CMOS) incompatibility, hindering simultaneous optimization of power efficiency, speed, stability, and integrability. Here, we demonstrate a self-powered reconfigurable photodiode based on a bipolar WSe2 channel and a sub-20-nm ferroelectric HfxZr1−xO2 (HZO) layer. The device employs a split-gate architecture to generate polarity-switchable short-circuit photocurrent under photovoltaic mode, achieving ultralow programming energy (< 1 fJ), a switching speed of 50 μs, and weight retention exceeding 100 s. When deployed as a physical convolution kernel, the device performs in-sensor matrix-vector multiplication on incident light. In simulated edge-detection tasks, it achieves a remarkably low normalized mean squared error (~ 3.2 × 10−4), producing edge maps nearly indistinguishable from ideal software results. This work establishes an energy-efficient and self-driven hardware platform that unifies sensing, memory, and computation, realizing a practical framework for in-sensor computing.}
}