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Abstract Pixel binning is a technique, widely used in optical image acquisition and spectroscopy, in which adjacent detector elements of an image sensor are combined into larger pixels. This reduces the amount of data to be processed as well as the impact of noise, but comes at the cost of a loss of information. Here, we push the concept of binning to its limit by combining a large fraction of the sensor elements into a single “superpixel” that extends over the whole face of the chip. For a given pattern recognition task, its optimal shape is determined from training data using a machine learning algorithm. We demonstrate the classification of optically projected images from the MNIST dataset on a nanosecond timescale, with enhanced dynamic range and without loss of classification accuracy. Our concept is not limited to imaging alone but can also be applied in optical spectroscopy or other sensing applications.

Paper is a promising substrate for flexible and environmentally sustainable electronic devices. Here, the authors combine chemical vapor deposition of MoS2 with inkjet printing of a hexagonal boron nitride (hBN) dielectric and silver electrodes, to fabricate flexible MoS2 field-effect transistors on paper, and then combine the latter with printed graphene resistors and silver interconnects to create inverters, logic gates and current mirrors.

The emission of light from correlated excitonic complexes has been recently observed in atomically thin transition metal dichalcogenides. Here, the authors report electroluminescence generated by a pulsed gate voltage from excitons, trions, and biexcitons in monolayer WSe2 and WS2 encapsulated with hBN.

Strain is an effective tool to tune the optoelectronic properties of two-dimensional materials. Here, the authors demonstrate that second harmonic generation can be used to extract the full strain tensor of MoS2 and to spatially image its two-dimensional strain field.