Zhou Jia, Li Wen, Chen Ye, Qian Haowen, Lin Yen-Hung, Li Ruipeng, Wang Zhen, Wang Jin, Shi Wei, Tao Xianwang, Tao Youtian, Ling Haifeng, Huang Wei, Yi Mingdong
State Key Laboratory of Flexible Electronics, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, China.
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China.
Light Sci Appl. 2025 Sep 8;14(1):309. doi: 10.1038/s41377-025-01986-9.
As the demand for edge platforms in artificial intelligence increases, including mobile devices and security applications, the surge in data influx into edge devices often triggers interference and suboptimal decision-making. There is a pressing need for solutions emphasizing low power consumption and cost-effectiveness. In-sensor computing systems employing memristors face challenges in optimizing energy efficiency and streamlining manufacturing due to the necessity for multiple physical processing components. Here, we introduce low-power organic optoelectronic memristors with synergistic optical and mV-level electrical tunable operation for a dynamic "control-on-demand" architecture. Integrating signal sensing, featuring, and processing within the same memristors enables the realization of each in-sensor analogue reservoir computing module, and minimizes circuit integration complexity. The system achieves 97.15% fingerprint recognition accuracy while maintaining a minimal reservoir size and ultra-low energy consumption. Furthermore, we leverage wafer-scale solution techniques and flexible substrates for optimal memristor fabrication. By centralizing core functionalities on the same in-sensor platform, we propose a resilient and adaptable framework for energy-efficient and economical edge computing.
随着包括移动设备和安全应用在内的人工智能边缘平台需求的增加，流入边缘设备的数据激增往往会引发干扰和次优决策。迫切需要强调低功耗和成本效益的解决方案。由于需要多个物理处理组件，采用忆阻器的传感器内计算系统在优化能源效率和简化制造方面面临挑战。在此，我们引入了具有协同光学和毫伏级电可调操作的低功耗有机光电忆阻器，用于动态“按需控制”架构。在同一忆阻器内集成信号传感、特征提取和处理功能，能够实现每个传感器内模拟储层计算模块，并将电路集成复杂性降至最低。该系统在保持最小储层规模和超低能耗的同时，实现了97.15%的指纹识别准确率。此外，我们利用晶圆级解决方案技术和柔性基板来优化忆阻器制造。通过将核心功能集中在同一传感器内平台上，我们提出了一个用于节能和经济高效边缘计算的弹性且适应性强的框架。
