Chen Chunsheng, Zhou Yaoqiang, Tong Lei, Pang Yue, Xu Jianbin
Department of Electronic Engineering and Materials Science and Technology Research Center, The Chinese University of Hong Kong, Hong Kong SAR, China.
Adv Mater. 2025 Jan;37(2):e2400332. doi: 10.1002/adma.202400332. Epub 2024 May 20.
The quantity of sensor nodes within current computing systems is rapidly increasing in tandem with the sensing data. The presence of a bottleneck in data transmission between the sensors, computing, and memory units obstructs the system's efficiency and speed. To minimize the latency of data transmission between units, novel in-memory and in-sensor computing architectures are proposed as alternatives to the conventional von Neumann architecture, aiming for data-intensive sensing and computing applications. The integration of 2D materials and 2D ferroelectric materials has been expected to build these novel sensing and computing architectures due to the dangling-bond-free surface, ultra-fast polarization flipping, and ultra-low power consumption of the 2D ferroelectrics. Here, the recent progress of 2D ferroelectric devices for in-sensing and in-memory neuromorphic computing is reviewed. Experimental and theoretical progresses on 2D ferroelectric devices, including passive ferroelectrics-integrated 2D devices and active ferroelectrics-integrated 2D devices, are reviewed followed by the integration of perception, memory, and computing application. Notably, 2D ferroelectric devices have been used to simulate synaptic weights, neuronal model functions, and neural networks for image processing. As an emerging device configuration, 2D ferroelectric devices have the potential to expand into the sensor-memory and computing integration application field, leading to new possibilities for modern electronics.
当前计算系统中传感器节点的数量随着传感数据量的增加而迅速增长。传感器、计算单元和存储单元之间的数据传输瓶颈阻碍了系统的效率和速度。为了最小化单元之间的数据传输延迟，人们提出了新型的内存内和传感器内计算架构，作为传统冯·诺依曼架构的替代方案，以用于数据密集型传感和计算应用。由于二维铁电体具有无悬键表面、超快极化翻转和超低功耗等特性，二维材料与二维铁电材料的集成有望构建这些新型传感和计算架构。在此，对用于传感和内存神经形态计算的二维铁电器件的最新进展进行综述。回顾了二维铁电器件的实验和理论进展，包括集成无源铁电体的二维器件和集成有源铁电体的二维器件，随后介绍了感知、记忆和计算应用的集成。值得注意的是，二维铁电器件已被用于模拟突触权重、神经元模型函数以及用于图像处理的神经网络。作为一种新兴的器件配置，二维铁电器件有潜力扩展到传感器-内存和计算集成应用领域，为现代电子学带来新的可能性。
