Vijjapu Mani Teja, Fouda Mohammed E, Agambayev Agamyrat, Kang Chun Hong, Lin Chun-Ho, Ooi Boon S, He Jr-Hau, Eltawil Ahmed M, Salama Khaled N
Sensors lab, Advanced Membranes and Porous Materials Center, Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
Communication and Computing Systems Lab, Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
Light Sci Appl. 2022 Jan 1;11(1):3. doi: 10.1038/s41377-021-00686-4.
Neuromorphic vision sensors have been extremely beneficial in developing energy-efficient intelligent systems for robotics and privacy-preserving security applications. There is a dire need for devices to mimic the retina's photoreceptors that encode the light illumination into a sequence of spikes to develop such sensors. Herein, we develop a hybrid perovskite-based flexible photoreceptor whose capacitance changes proportionally to the light intensity mimicking the retina's rod cells, paving the way for developing an efficient artificial retina network. The proposed device constitutes a hybrid nanocomposite of perovskites (methyl-ammonium lead bromide) and the ferroelectric terpolymer (polyvinylidene fluoride trifluoroethylene-chlorofluoroethylene). A metal-insulator-metal type capacitor with the prepared composite exhibits the unique and photosensitive capacitive behavior at various light intensities in the visible light spectrum. The proposed photoreceptor mimics the spectral sensitivity curve of human photopic vision. The hybrid nanocomposite is stable in ambient air for 129 weeks, with no observable degradation of the composite due to the encapsulation of hybrid perovskites in the hydrophobic polymer. The functionality of the proposed photoreceptor to recognize handwritten digits (MNIST) dataset using an unsupervised trained spiking neural network with 72.05% recognition accuracy is demonstrated. This demonstration proves the potential of the proposed sensor for neuromorphic vision applications.
神经形态视觉传感器在为机器人技术和隐私保护安全应用开发节能智能系统方面极为有益。迫切需要能模仿视网膜光感受器的设备，这些光感受器将光照编码为一系列脉冲来开发此类传感器。在此，我们开发了一种基于混合钙钛矿的柔性光感受器，其电容随光强成比例变化，模仿了视网膜的视杆细胞，为开发高效的人工视网膜网络铺平了道路。所提出的器件由钙钛矿（甲基溴化铅铵）和铁电三元共聚物（聚偏二氟乙烯 - 三氟乙烯 - 氯氟乙烯）的混合纳米复合材料构成。由制备的复合材料制成的金属 - 绝缘体 - 金属型电容器在可见光谱的各种光强下表现出独特的光敏电容行为。所提出的光感受器模仿了人类明视觉的光谱灵敏度曲线。该混合纳米复合材料在环境空气中稳定129周，由于混合钙钛矿被封装在疏水性聚合物中，复合材料没有可观察到的降解。利用无监督训练的脉冲神经网络对所提出的光感受器识别手写数字（MNIST）数据集的功能进行了演示，识别准确率为72.05%。这一演示证明了所提出的传感器在神经形态视觉应用中的潜力。
