Multimodal Piezotronic‐Gated Ferroelectric Tunneling Junctions for High‐Resolution Tactile Imaging
Mengmeng Jia, Di Guo, Pengwen Guo, Jiong Pan, Xia Liu, Lele Ren, Mengshuang Chi, Yulong Wang 等 12 位
Chinese Academy of Sciences Beijing Institute of Nanoenergy and Nanosystems University of Chinese Academy of Sciences Taiyuan University of Technology
阅读操作
确认中在文库中上传 PDF 后可生成中文音频讲解。
摘要与影响
Multimodal perception is critical for applications such as virtual reality and human interaction by integrating information to improve the accuracy and reliability of perceiving the external environment. However, conventional single‐mode mechanical sensors frequently encounter the trade‐off between sensitivity and dynamic range, limiting their performance in complex tactile environments. Here, we propose an ultrasensitive multimodal strain sensor based on a piezotronic‐gated ferroelectric tunneling junction (PFTJ). By utilizing ferroelectric polarization to set the initial interfacial barrier and strain‐induced piezoelectric potential for dynamic modulation, the PFTJ achieves simultaneous control over tunneling barrier height and width. This dual‐mode mechanism yields exceptional gauge factors of 2.4 × 10 4 and 9.0 × 10 5 in the polarization‐down and polarization‐up states at strains of −0.78‰ and −1.34‰, respectively. The device can precisely sense subtle textures, with its multimodal nature increasing the silhouette score by 3.6 times. Furthermore, the PFTJ demonstrates state‐of‐the‐art real‐time tactile imaging, achieving a high frame rate of 89 frames per second (FPS) and an unprecedented pixel density of 6.6 × 10 7 pixels per inch (PPI). Unlike electrode‐defined crossbar arrays, the pixels here are defined by nanoflake locations and activated synchronously during scanning, ensuring a 99.95% active pixel percentage and a millisecond response time. This work provides a fundamental framework for multimodal PFTJs, advancing the development of next‐generation electromechanical sensors for high‐performance tactile imaging and adaptive human–machine interfaces.
逐年被引趋势
暂无年度引用数据
关键指标
同类平均 = 1
同领域 · 同年份 · 同类型
Google Scholar 与 OpenAlex 的被引统计范围不同,数值存在差异属正常。
AI 辅助阅读
依据:摘要
可就本文提问;依据不足时会说明。
学术脉络
学科主题
工程Advanced Sensor and Energy Harvesting Materials
Ferroelectric and Negative Capacitance Devices · Ferroelectric and Piezoelectric Materials