Quantum Dot technology enables machine vision applications

Applications

Due to their low cost and high performance, PbS QDs greatly expand the use cases of SWIR imaging in machine vision applications. QD-equipped sensors can “see” through sealed containers to monitor fill levels or check for damage, and can penetrate silicon to facilitate contact quality inspection in semiconductor component manufacturing. At certain wavelengths, SWIR light is strongly absorbed by water, meaning that images with a high water content appear black under such conditions. This allows the cameras to detect, for example, damaged or spoiled fruit or black ice on roads.

In particular, other wavelengths such as NIR and MWIR – also commonly used in machine vision – are unable to perform similar functions.

SWIR sensors also offer improved performance in all light and weather conditions. While other wavelengths are scattered by dust, fog and precipitation, SWIR light is unaffected, meaning these cameras perform optimally regardless of the conditions. This gives them great potential in defense and security, as well as in automotive sensors such as Light Detection and Ranging (LiDAR) systems – extending the effective range of such sensors to 500m.

QDs also have the potential to benefit from other emerging technologies. A growing number of manufacturers are integrating AI into their machine vision systems, leveraging its power to improve automation and efficiency. AI offers visual systems a tool to recognize patterns around them, comparing captured images to an evolving database of reference images. For example, using AI, machine vision systems can use natural language processing to better understand and interpret label text. As technology advances, more exciting applications such as deep learning will enable the automation of many complex processes.

This acceleration of AI potential will be aided by QDs enhancing the data it accesses. AI works based on analyzing data sets and identifying patterns. That data set is all it knows, which means its effectiveness depends on the quality of that information. QD-equipped sensors capable of capturing and analyzing vast amounts of “invisible” data and perceiving the world around them more accurately than existing alternatives will improve the data that AI can access and make these tools significantly more powerful.

This is not to mention the potential of this technology in other sectors. In medicine, CT scans are used to detect hidden conditions under the patient’s skin without the need for intrusive surgery. In consumer electronics, SWIR capability could facilitate spectroscopy on next-generation smartphones or enhance virtual and augmented reality devices. In photovoltaics, solar cells equipped with QDs can more efficiently generate excitons than silicon or copper indium gallium selenide alternatives due to their high photostability and tunable band gaps. The use of QDs effectively expands the fraction of light that solar cells can convert from 32-33% to a potential 66%.

Challenges to overcome

The main barrier to mass adoption of QD technology for machine vision applications is its scalability. Many developers struggle with poor control of nanoparticle synthesis, environmental degradation, and limited batch-to-batch consistency. Overcoming these challenges requires specialized expertise in QD synthesis and scaling, surface chemistry, ink formulation, manufacturing, and testing.

Moreover, the inherent toxicity of the best performing QDs has so far limited their use in mass market applications. To achieve the highest possible efficiency, CTs must have coatings made of substances such as cadmium or lead, which are strictly regulated in many markets due to their harmful effects. Although alternative solutions are available that replace heavy metals with substances such as indium, they generally offer lower performance. The best performing lead-free QDs are responsible for wavelengths up to 1550 nm, which, although suitable for certain SWIR sensing applications, limits them from tasks requiring higher sensitivity.

Road lighting

QD technology is not some far-fetched concept; already in use today. Several major industry players have already introduced QD-based imaging devices for industrial use as an alternative to their existing InGaAs technology. These devices have demonstrated small global shutter pixels with less than 2000 nm pixel pitch while offering high performance and a significant reduction in manufacturing costs.

As QD technology advances and barriers to scale-up are overcome, these semiconductor nanocrystals will be increasingly used to make SWIR imaging and sensors more cost-effective. In addition, the high tunability of QDs can also allow them to tap into MWIR wavelengths, offering a cheaper alternative to cameras that often cost 10-100 times the cost of SWIR equivalents.

Although QDs are unlikely to replace InGaAs for SWIR sensing in the near future, this technology is ideally positioned to complement it. By making SWIR capabilities more accessible and affordable, QDs are bringing the future of machine vision to fruition – and that future is looking ever brighter.

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