such as healthcare, biometrics, autonomous driving, agriculture, chemical detection, and food inspection, among others. IDTechEx predicts that the growing importance of autonomous technologies will cause the emerging image sensor market to exceed $500 million by 2033.
Extending the Spectral Range:
Conventional CMOS detectors for visible light are prevalent in robotics, industrial imaging inspection, and consumer electronics; however, significant opportunities exist for more complex image sensors that offer capabilities beyond simply acquiring red, green, and blue (RGB) intensity values. Currently, considerable effort is being devoted to developing new image sensor technologies capable of detecting aspects of light beyond the visible range, in the short-wave infrared (SWIR, 1000 nm–2000 nm) range. Extending the detection wavelength to the SWIR range offers many advantages, as it allows for the differentiation of objects and materials that appear visually similar or identical in the visible range. This substantially improves recognition accuracy and paves the way for better and more functional image detection.
A key trend is the development of much cheaper alternatives to the very expensive InGaAs sensors for imaging in the short-wave infrared (SWIR, 1000–2000 nm) spectral region, which will open this capability to a much wider range of applications. This includes autonomous vehicles, where SWIR imaging helps distinguish objects and materials that appear similar in the visible spectrum, while also reducing the interference of dust and haze.
Several emerging, non-InGaAs-based SWIR technologies are competitive. These include hybrid imaging sensors, in which a thin additional layer of organic semiconductors or quantum dots absorbs light and is placed on top of a CMOS readout circuit to increase the detection range of wavelengths in the SWIR region. Another technology is extended-range silicon, whose properties are modified to extend the absorption range beyond the limitations of its band gap. Currently dominated by expensive InGaAs sensors, these new approaches promise a substantial price reduction, which is expected to encourage the adoption of SWIR imaging for new applications such as autonomous vehicles.
New image sensors enable new functions.
In addition to capturing images across a wider spectral range, other innovations include capturing images over a larger area, acquiring spectral data at each pixel, and simultaneously increasing temporal resolution and dynamic range. One promising technology in this area is event-based vision. With conventional frame-based imaging, high temporal resolution produces large amounts of data that require intensive computer processing. Event-based vision solves this challenge by presenting a completely new way of thinking about optical information acquisition, in which each pixel of the sensor reports timestamps that correspond to changes in intensity. In this way, event-based vision can combine higher temporal resolution of rapidly changing image regions with significantly reduced data transfer and post-processing requirements.
Another promising innovation is the increasing miniaturization and conformality of sensor technology, which makes it easier than ever to integrate a sensor into a robotic arm, industrial inspection machinery, or consumer electronics. These are some of the applications targeted by the burgeoning market for miniaturized spectrometers. Driven by the growth of smart electronics and Internet of Things devices, low-cost miniaturized spectrometers are becoming increasingly relevant in various sectors. The complexity and functionality of standard visible light sensors can be significantly enhanced by integrating miniaturized spectrometers capable of detecting from the visible region to the SWIR region of the spectrum with high spectral resolution. The future envisioned by Fraunhofer researchers is a spectrometer that weighs just one gram and costs one dollar. Miniaturized spectrometers are expected to offer cost-effective solutions for improving autonomous efficiency through greater differentiation of materials, objects, and colors.
