Hao Ni

Abstract In the quest for emerging in-sensor computing, materials that respond to optical stimuli in conjunction with non-volatile phase transition are highly desired for realizing bioinspired neuromorphic vision components. Here, we report a non-volatile multi-level control of VO 2 films by oxygen stoichiometry engineering under ultraviolet irradiation. Based on the reversible regulation of VO 2 films using ultraviolet irradiation and electrolyte gating, we demonstrate a proof-of-principle neuromorphic ultraviolet sensor with integrated sensing, memory, and processing functions at room temperature, and also prove its silicon compatible potential through the wafer-scale integration of a neuromorphic sensor array. The device displays linear weight update with optical writing because its metallic phase proportion increases almost linearly with the light dosage. Moreover, the artificial neural network consisting of this neuromorphic sensor can extract ultraviolet information from the surrounding environment, and significantly improve the recognition accuracy from 24% to 93%. This work provides a path to design neuromorphic sensors and will facilitate the potential applications in artificial vision systems.

Abstract With their unique advantages in portability, shape adaptability, and human friendly surfaces, flexible electronics pave the way for the implementation of wearable electronic textiles and human–machine interfaces. Although organic materials are promising for flexible devices because of the low‐cost manufacturing and inherent flexibility, they meet challenges in harsh environments such as ultraviolet (UV) irradiation, which limits their applicability in UV sensors. Here, a flexible UV neuromorphic sensor is presented using inorganic vanadium dioxide (VO 2 ) films grown on mica substrates. The flexible device shows UV photoinduced nonvolatile phase transition, and can be reversibly modulated using electrolyte gating. The optical responses remain almost unchanged after 10 000 bending cycles or at small bending radius, exhibiting high tolerance to the bending deformation. Besides, the variations in image recognition accuracy under different bending conditions keep within 1.6%, indicating that the device can be adapted to various deformation conditions. By constructing near‐/in‐sensor computing architectures using the flexible VO 2 neuromorphic sensors with photoinduced nonvolatile phase transition, both static image processing and motion detection are realized without redundant and massive information transfer. This result lays the foundation for the development of flexible UV neuromorphic sensors.

To ensure the photovoltaic (PV) arrays under partial shading condition(PSC) could still output maximum power quickly and efficiently, this work presents a modified hybrid maximum power point tracking (MPPT) method, which applies artificial neural network (ANN) to the modified perturb and observe (MP&O). Instead of using expensive illumination intensity sensors directly, the illumination intensity on each module in the PV array can be obtained indirectly by sampling the specific points of their own cheaper voltage-current sensors. ANN uses indirect illumination intensity to predict the optimal voltage areas of the global maximum power point (GMPP). Based on the areas, MP&O adopts a adaptive step size strategy to obtain GMPP. By modeling and simulation in Matlab/Simulink, it is shown that the tracking time and efficiency of the proposed method in this work can reach 0.026s and 99.87% respectively. Compared with other methods, the method has faster speed, higher efficiency, smaller fluctuation and lower complexity.