RESUMO
MoS2 nanoswitches have shown superb ultralow switching energies without excessive leakage currents. However, the debate about the origin and volatility of electrical switching is unresolved due to the lack of adequate nanoimaging of devices in operando. Here, three optical techniques are combined to perform the first noninvasive in situ characterization of nanosized MoS2 devices. This study reveals volatile threshold resistive switching due to the intercalation of metallic atoms from electrodes directly between Mo and S atoms, without the assistance of sulfur vacancies. A "semi-memristive" effect driven by an organic adlayer adjacent to MoS2 is observed, which suggests that nonvolatility can be achieved by careful interface engineering. These findings provide a crucial understanding of nanoprocess in vertically biased MoS2 nanosheets, which opens new routes to conscious engineering and optimization of 2D electronics.
RESUMO
In the last decade, Cu2ZnSnS4 (CZTS) has been a promising earth-abundant, nontoxic candidate material for absorption layers within thin-film solar cells. One major issue preventing this type of solar cells from achieving competitive efficiency is impurity phases and structural defects in the bulk of the absorber; as a four-element compound, the formation of CZTS is highly sensitive to synthesis conditions. The impurity phases and defects differ by the fabrication method, and thus experimental characterization is vital for the successful development of CZTS photovoltaics. In this work, we characterize CZTS nanoparticles obtained by the hot-injection method and a standard N2/S annealing procedure. Phase-pure kesterite CZTS samples in the desired compositional range were characterized by standard means, i.e., Raman spectroscopy, X-ray diffraction, and energy-dispersive X-ray spectroscopy. However, using synchrotron X-ray diffraction with Rietveld refinement, we show that the as-synthesized nanoparticles consist of a mixture of the tetragonal and the fully disordered cubic sphalerite phase and transform into the tetragonal structure after heat treatment. Sn vacancies are seen in the annealed samples. X-ray total scattering with pair distribution function analysis furthermore suggests the presence of a nanostructured CZTS phase along with a bulk material. Finally, this study compares the benefits of applying synchrotron radiation instead of a standard laboratory X-ray diffraction when characterizing highly complex materials.
RESUMO
Machine vision technology has taken huge leaps in recent years, and is now becoming an integral part of various intelligent systems, including autonomous vehicles and robotics. Usually, visual information is captured by a frame-based camera, converted into a digital format and processed afterwards using a machine-learning algorithm such as an artificial neural network (ANN)1. The large amount of (mostly redundant) data passed through the entire signal chain, however, results in low frame rates and high power consumption. Various visual data preprocessing techniques have thus been developed2-7 to increase the efficiency of the subsequent signal processing in an ANN. Here we demonstrate that an image sensor can itself constitute an ANN that can simultaneously sense and process optical images without latency. Our device is based on a reconfigurable two-dimensional (2D) semiconductor8,9 photodiode10-12 array, and the synaptic weights of the network are stored in a continuously tunable photoresponsivity matrix. We demonstrate both supervised and unsupervised learning and train the sensor to classify and encode images that are optically projected onto the chip with a throughput of 20 million bins per second.
