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Neuromorphic event-based image sensors capture only the dynamic motion in a scene, which is then transferred to computation units for motion recognition. This approach, however, leads to time latency and can be power consuming. Here we report computational event-driven vision sensors that capture and directly convert dynamic motion into programmable, sparse and informative spiking signals. The sensors can be used to form a spiking neural network for motion recognition. Each individual vision sensor consists of two parallel photodiodes with opposite polarities and has a temporal resolution of 5 mu s. In response to changes in light intensity, the sensors generate spiking signals with different amplitudes and polarities by electrically programming their individual photoresponsivity. The non-volatile and multilevel photoresponsivity of the vision sensors can emulate synaptic weights and can be used to create an in-sensor spiking neural network. Our computational event-driven vision sensor approach eliminates redundant data during the sensing process, as well as the need for data transfer between sensors and computation units. A spiking neural network that is based on event-driven vision sensors can be created using two parallel photodiodes of opposite polarities that output programmable spike signal trains in response to changes in light intensity.

Chalcogenide phase-change materials (PCMs) exhibit optical phonons at terahertz (THz) frequencies, which can be used for studying basic properties of the phase transition and which lead to a strong dielectric contrast that could be exploited for THz photonics applications. Here, we demonstrate that the phonons of PCMs can be studied by frequency-tunable THz scattering-type scanning near-field optical microscopy (s-SNOM). Specifically, we perform spectroscopic THz nanoimaging of a PCM sample comprising amorphous and crystalline phases. We observe phonon signatures, yielding strong s-SNOM signals and, most important, clear spectral differences between the amorphous and crystalline PCM, which allows for distinguishing the PCM phases with high confidence on the nanoscale. We also found that the spectral signature can be enhanced, regarding both signal strength and spectral contrast, by increasing the radius of the probing tip. From a general perspective, our results establish THz s-SNOM for nanoscale structural and chemical mapping based on local phonon spectroscopy.

The AgInSbTe memristor shows gradual resistance tuning characteristics, which makes it a potential candidate to emulate biological plastic synapses. The working mechanism of the device is complex, and both intrinsic charge-trapping mechanism and extrinsic electrochemical metallization effect are confirmed in the AgInSbTe memristor. Mathematical model of the AgInSbTe memristor has not been given before. We propose the flux-voltage controlled memristor model. With piecewise linear approximation technique, we deliver the flux-voltage controlled memristor model of the AgInSbTe memristor based on the experiment data. Our model fits the data well. The flux-voltage controlled memristor model and the piecewise linear approximation method are also suitable for modeling other kinds of memristor devices based on experiment data.

The AgInSbTe memristor shows gradual resistance tuning characteristics, which makes it a potential candidate to emulate biological plastic synapses. The working mechanism of the device is complex, and both intrinsic charge-trapping mechanism and extrinsic electrochemical metallization effect are confirmed in the AgInSbTe memristor. Mathematical model of the AgInSbTe memristor has not been given before. We propose the flux-voltage controlled memristor model. With piecewise linear approximation technique, we deliver the flux-voltage controlled memristor model of the AgInSbTe memristor based on the experiment data. Our model fits the data well. The flux-voltage controlled memristor model and the piecewise linear approximation method are also suitable for modeling other kinds of memristor devices based on experiment data.