Technology A device converts shortwave infrared light to visible light


Scientists have designed an organic dye-based device that can see light in the shortwave infrared (SWIR) range. T e device is easy to make using cheap materials, and is stable at high temperatures, which will see it applied widely in imaging and sensing systems. T e human eye detects a very narrow


segment of the electromagnetic spectrum, from around 400 to 700nm. T e SWIR region, on the other hand, extends from 1,000 to 2,500nm. Specially-designed cameras can produce images of objects that refl ect light in the SWIR region. T ey are used for improving night vision, especially in airborne remote sensing and deep-tissue imaging. T e cameras also help assess the composition and quality of silicon wafers, building structures and even food produce. “Such cameras are typically diffi cult to


manufacture and are quite expensive, as they are made of inorganic semiconductor


shortwave infrared light to visible light. In it, SWIR light is absorbed by the squaraine dye in the photodetector (PD), producing electrical charge, which is directed into organic light-emitting diodes (OLEDs) resulting in the emission of visible light. T e team had to manipulate the molecular


Synthesis of squaraine dyes SQ1-SQ4


photodiode arrays interconnected with read-out integrated circuitry,” says Roland Hany of the Swiss Federal Laboratories for Materials Science and Technology. Hany worked with colleagues in Switzerland and Italy to design an organic dye-based SWIR upconversion device that effi ciently converts


composition of several squaraine dyes to get them to absorb specifi c wavelengths. Ultimately, they synthesised squaraine dyes that absorb SWIR light beyond 1,200nm and remain stable to 200°C. “Night-vision devices can be directly


integrated into car windscreens without aff ecting the visual fi eld,” said Hany. T e team is now working on shiſt ing


the dye’s absorption further into the SWIR range. T ey are also using machine-learning techniques to fi nd new dye molecules capable of sensing SWIR waves, as well as improve the device’s stability and sensitivity.


Layered-materials chip classifi es images a thousand times faster than conventional machine-vision systems


Researchers at the Vienna University of Technology have developed an image sensor with an integrated artifi cial neural network (ANN) capable of learning and classifying images within nanoseconds. T e chip is a thousand times faster and uses a lot less power than conventional vision technologies. T is is because the sensor’s fast capture and processing of images does not consume power, since the photons themselves provide the energy for the output current. T e researchers, supported by the European


research project the Graphene Flagship, devised sensors containing nine pixels, or ‘neurons’, arranged in a 3x3 array. Every pixel in turn consists of three photodiodes with three outputs. Each photodiode links its pixel to the other eight pixels. T e current from each photodiode is governed by the intensity of incoming light. Each neuron sums the individual currents coming from the other eight neurons, with the combined values then fed into a computer. T e device can classify images aſt er a series


of training processes, but it can also recognise a characteristic component or structure of an image from input data, without additional information.


The ANN-enabled image sensor is capable of classifying images within nanoseconds T e speed sets this device apart from


conventional machine vision. Conventional technology is usually capable of processing up to 100 frames per second, with some faster systems capable of 1,000 frames per second. By comparison, this system works at an equivalent speed of 20 million frames per second. In combination with other technologies, the


device can fi nd many uses, including in fl uid dynamics, high-energy physics, combustion processes or mechanical breakdown.


“We are considering other ideas: like


improving the light absorption or extending the spectral range into the infrared. In principle, the capabilities of this device are not only limited to visual data – any kind of data could be (pre)processed with an artifi cial neural network in the sensor itself. For example, audio or olfactory neuromorphic sensors could be developed for rapid on-chip processing,” said Lukas Mennel, author of the study.


www.electronicsworld.co.uk May 2021 05


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