Key Laboratory of Wide Band Gap Semiconductor Technology, School of Advanced Materials and Nanotechnology, Xidian University, Xi'an, 710071, China.
Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China.
Small. 2022 Jun;18(23):e2201111. doi: 10.1002/smll.202201111. Epub 2022 May 9.
The biological nervous system possesses a powerful information processing capability, and only needs a partial signal stimulation to perceive the entire signal. Likewise, the hardware implementation of an information processing system with similar capabilities is of great significance, for reducing the dimensions of data from sensors and improving the processing efficiency. Here, it is reported that indium-gallium-zinc-oxide thin film phototransistors exhibit the optoelectronic switching and light-tunable synaptic characteristics for in-sensor compression and computing. Phototransistor arrays can compress the signal while sensing, to realize in-sensor compression. Additionally, a reservoir computing network can also be implemented via phototransistors for in-sensor computing. By integrating these two systems, a neuromorphic system for high-efficiency in-sensor compression and computing is demonstrated. The results reveal that even for cases where the signal is compressed by 50%, the recognition accuracy of reconstructed signal still reaches ≈96%. The work paves the way for efficient information processing of human-computer interactions and the Internet of Things.
生物神经系统具有强大的信息处理能力，仅需部分信号刺激即可感知整个信号。同样，具有类似能力的信息处理系统的硬件实现也具有重要意义，可以减少传感器的数据维度并提高处理效率。在这里，据报道，铟镓锌氧化物薄膜光电晶体管表现出光电开关和光可调突触特性，可用于传感器内压缩和计算。光电晶体管阵列可以在传感的同时压缩信号，从而实现传感器内压缩。此外，还可以通过光电晶体管实现储层计算网络，用于传感器内计算。通过整合这两个系统，展示了用于高效传感器内压缩和计算的神经形态系统。结果表明，即使信号被压缩了 50%，重建信号的识别准确率仍达到 ≈96%。这项工作为高效的人机交互和物联网信息处理铺平了道路。
