Dang Bingjie, Liu Keqin, Wu Xulei, Yang Zhen, Xu Liying, Yang Yuchao, Huang Ru
Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.
Center for Brain Inspired Chips, Institute for Artificial Intelligence, Peking University, Beijing, 100871, China.
Adv Mater. 2023 Sep;35(37):e2204844. doi: 10.1002/adma.202204844. Epub 2022 Sep 28.
The recent advances in optic neuromorphic devices have led to a subsequent rise in use for construction of energy-efficient artificial-vision systems. The widespread use can be attributed to their ability to capture, store, and process visual information from the environment. The primary limitations of existing optic neuromorphic devices include nonlinear weight updates, cross-talk issues, and silicon process incompatibility. In this study, a highly linear, light-tunable, cross-talk-free, and silicon-compatible one-phototransistor-one-memristor (1PT1R) optic memristor is experimentally demonstrated for the implementation of an optic artificial neural network (OANN). For optic image recognition in the experiment, an OANN is constructed using a 16 × 3 1PT1R memristor array, and it is trained on an online platform. The model yields an accuracy of 99.3% after only ten training epochs. The 1PT1R memristor, which shows good performance, demonstrates its ability as an excellent hardware solution for highly efficient optic neuromorphic and edge computing.
近年来，光学神经形态器件的进展促使其在构建节能人工视觉系统中的应用日益增多。其广泛应用归因于它们能够捕获、存储和处理来自环境的视觉信息。现有光学神经形态器件的主要局限性包括非线性权重更新、串扰问题以及与硅工艺不兼容。在本研究中，通过实验展示了一种高度线性、光可调、无串扰且与硅兼容的单光晶体管-单忆阻器（1PT1R）光学忆阻器，用于实现光学人工神经网络（OANN）。在实验中的光学图像识别方面，使用16×3的1PT1R忆阻器阵列构建了一个OANN，并在在线平台上对其进行训练。该模型仅经过十个训练周期后，准确率就达到了99.3%。性能良好的1PT1R忆阻器证明了其作为高效光学神经形态和边缘计算优秀硬件解决方案的能力。
