Image sensing is a highly important capability, used in applications ranging from webcams and smartphone cameras to autonomous vehicles and industrial inspection. This report from IDTechEx comprehensively explores the market for emerging image sensors, covering a diverse range of technologies that span from thin-film flexible photodetectors to event-based vision.

 

 

While conventional CMOS detectors for visible light are well established and somewhat commoditized, at least for low value applications, there is an extensive opportunity for more complex image sensors that offer capabilities beyond that of simply acquiring red, green and blue (RGB) intensity values. As such, extensive effort is currently being devoted to developing emerging image sensor technologies that can detect aspects of light beyond human vision. This includes imaging over a broader spectral range, over a larger area, acquiring spectral data at each pixel, and simultaneously increasing temporal resolution and dynamic range.

 

Much of this opportunity stems from the ever-increasing adoption of machine vision, in which image analysis is performed by computational algorithms. Machine learning requires as much input data as possible to establish correlations that can facilitate object identification and classification, so acquiring optical information over a different wavelength range, or with spectral resolution for example, is highly advantageous.

 

Of course, emerging image sensor technologies offer many other benefits. Depending on the technology this can include similar capabilities at a lower cost, increased dynamic range, improve temporal resolution, spatially variable sensitivity, global shutters at high resolution, reducing the unwanted influence of scattering, flexibility/conformality and more. A particularly important trend is the development of much cheaper alternatives to very expensive InGaAs sensors for imaging in the short-wave infra-red (SWIR, 1000 - 2000 nm) spectral region, which will open up this capability to a much wider range of applications. This includes autonomous vehicles, in which SWIR imaging assists with distinguishing objects/materials that appear similar in the visible spectrum, while also reducing scattering from dust and fog.

 

The report covers the following technologies:

 

 

Hybrid image sensors. Adding an additional light absorbing layer on top of a CMOS read-out circuit is a hybrid approach that utilizes either organic semiconductors or quantum dots to increase the spectral sensitivity into the SWIR region. Currently dominated by expensive InGaAs sensors, this new technology promises a substantial price reduction and hence adoption of SWIR imaging for new applications such as autonomous vehicles.

 

Extended-range silicon. Given the very high price of InGaAs sensors, there is considerable motivation to develop much lower cost alternatives that can detect light towards the lower end of the SWIR spectral region. Such SWIR sensors could then be employed in vehicles to provide better vision through fog and dust due to reduced scattering.

 

Thin film photodetectors. Detection of light over a large area, rather than at a single small detector, is highly desirable for acquiring biometric data and, if flexible, for imaging through the skin. At present, the high cost of silicon means that large-area image sensors can be prohibitively expensive. However, emerging approaches that utilize solution processable semiconductors offer a compelling way produce large-area conformal photodetectors. Printed organic photodetectors (OPDs) are the most developed approach, with under-display fingerprint detection being actively explored.

 

Event-based vision: Autonomous vehicles, drones and high-speed industrial applications require image sensing with a high temporal resolution. However, with conventional frame-based imaging a high temporal resolution produces vast amounts of data that requires computationally intensive processing. Event-based vision, also known as dynamic vision sensing (DVS), is an emerging technology that resolves this challenge. It is a completely new way of thinking about obtaining optical information, in which each sensor pixel reports timestamps that correspond to intensity changes. As such, event-based vision can combine greater temporal resolution of rapidly changing image regions, with much reduced data transfer and subsequent processing requirements.

 

Hyperspectral imaging: Obtaining as much information as possible from incident light is highly advantageous for applications that require object identification, since classification algorithms have more data to work with. Hyperspectral imaging, in which a complete spectrum is acquired at each pixel to product an (x, y, λ) data cube using a dispersive optical element and an image sensor, is a relatively established technology that has gained traction for precision agriculture and industrial process inspection. However, at present most hyperspectral cameras work on a line-scan principle, while SWIR hyperspectral imaging is restricted to relatively niche applications due to the high cost of InGaAs sensors. Emerging technologies look set to disrupt both these aspects, with snapshot imaging offering an alternative to line-scan cameras and with the new SWIR sensing technologies outlined above facilitating cost reduction and adoption for a wider range of applications.

 

Flexible x-ray sensors: X-ray sensors are well-established and highly important for medical and security applications. However, the difficulty in focusing x-rays means that sensors need to cover a large area. Furthermore, since silicon cannot effectively absorb x-rays a scintillator layer is commonly used. However, both these aspects increase sensor size and weight, making x-ray detectors bulky and unwieldy. Flexible x-ray sensors based on an amorphous silicon backplane offer a compelling alternative, since they would be lighter and conformal (especially useful for imaging curved body parts). Looking further ahead, direct x-ray sensors based on solution processable semiconductors offer reduced weight and complexity along with the potential for higher spatial resolution.

 

Wavefront imaging: Wavefront (or phase) imaging enables the extraction of phase information from incident light that is lost by a conventional sensor. This technique is currently used for niche applications such as optical component design/inspection and ophthalmology. However, recent advances have led to significant resolution improvements which will allow this technology to be applied somewhat more widely. Biological imaging is one of the more promising emerging applications, in which collecting phase along with intensity reduces the influence of scattering and thus enables better defined images.

 

In summary, increasing adoption of computational image analysis provides a great opportunity for image sensing technologies that offer capabilities beyond conventional CMOS sensors. This report offers a comprehensive overview of the market for emerging image sensor technologies and associated technical developments, covering a multitude of applications that range from autonomous vehicles to industrial quality control. Expect to see many of these exciting and innovative imaging technologies being rapidly adopted over the next decade.

 

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