In‐Situ Four‐Dimensional Neuromorphic Transistors for Spatiotemporal Fusion Information Perception

In the contemporary landscape of accelerating artificial intelligence (AI) development, multi‐dimensional information recognition has emerged as a critical enabler for enhancing both data computational efficiency and decision‐making precision. However, traditional multi‐dimensional recognition architectures exhibit a fundamental reliance on extensive hardware arrays and complex circuit topologies, posing significant challenges to hardware integration and system‐level miniaturization for AI‐based recognition systems. Here, for the first time, we propose an in situ 4D neuromorphic transistor (I‐FNT) and design a 4D spatiotemporal recognition system based on I‐FNT. Through dynamic encoding of the input port voltages of I‐FNT, programmable switching among three recognition modes (grayscale, depth, and time) is achieved, enabling cross‐dimensional information perception. Compared to existing multi‐dimensional information recognition systems, our 4D spatiotemporal recognition system significantly simplifies hardware while achieving 100% device integration gain. The I‐FNT‐integrated convolutional neural network (CNN) harnesses spatial (depth) information to achieve breakthrough performance in object recognition: 122% higher training efficiency and 345% faster training speed relative to conventional architectures, while attaining 94% accuracy. The system simultaneously facilitates object motion trajectory recognition, demonstrating comprehensive spatiotemporal processing capabilities. Therefore, I‐FNT provides an efficient and accurate novel solution for multi‐dimensional information recognition, representing a significant breakthrough for intelligent sensing and AI‐based recognition systems.