Emerging Image Sensor Technologies 2021-2031: Applications and Markets: IDTechEx

Autonomous technologies will lead the market for emerging image sensors to $360 million by 2031

Emerging Image Sensor Technologies 2021-2031: Applications and Markets

Innovative image sensor for autonomous vehicles, UAVs, precision agriculture and industrial automation. Includes organic photodetectors, SWIR image sensors, event-based vision, hyperspectral imaging, flexible x-ray detectors, and wavefront imaging

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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 than 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:
  • Quantum dots on silicon hybrid image sensors
  • Organic photodetectors on silicon hybrid image sensors
  • Emerging SWIR image sensor technologies
  • Organic and perovskite photodiodes (OPDs and PPDs)
  • Event-based vision
  • Hyperspectral imaging
  • Flexible x-ray image sensors
  • Wavefront imaging
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 is 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.
The following information is included within the report:
  • Executive summary & conclusions.
  • Detailed technical analysis of the emerging image sensor technologies outlined above.
  • Highly granular 10-year market forecasts, split by technology and subsequently by application. This includes over 40 individual forecast categories. Forecasts are expressed by both volume and revenue.
  • Technological/commercial readiness assessments, split by technology and application.
  • Commercial motivation for developing and adopting each of the emerging image sensing technologies.
  • Multiple application case studies for each image sensing technology.
  • SWOT analysis of each image sensing technology.
  • Overview of the key players within each technology category.
  • Over 25 company profiles, the majority based on recent primary interviews. These include a discussion of current status, technology, potential markets and business model, along with company financial information (where disclosed) and our SWOT analysis.
  • Selected highlights from academic research relevant to emerging image sensor technologies.
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Table of Contents
1.1.Key takeaways
1.2.Conventional image sensors: Market overview
1.3.Motivation for short-wave infra-red (SWIR) imaging
1.4.SWIR imaging: Incumbent and emerging technology options
1.5.Opportunities for SWIR image sensors
1.6.SWIR sensors: Application overview
1.7.OPD-on-CMOS hybrid image sensors
1.8.Quantum dots as optical sensor materials
1.9.Prospects for QD/OPD-on-CMOS detectors
1.10.Challenges for QD-Si technology for SWIR imaging
1.11.Overview of thin film organic and perovskite photodetectors
1.12.Applications of organic photodetectors.
1.13.Introduction to hyperspectral imaging
1.14.Overview of hyperspectral imaging
1.15.What is event-based vision?
1.16.Promising applications for event-based vision
1.17.Overview of event-based vision
1.18.Overview of wavefront imaging
1.19.Overview of flexible and direct x-ray image sensors
1.20.10-year market forecast for emerging image sensor technologies
1.21.10-year market forecast for emerging image sensor technologies (by volume)
1.22.10-year market forecast for emerging image sensor technologies (by volume, data table)
1.23.10-year market forecast for emerging image sensor technologies (by revenue)
1.24.10-year market forecast for emerging image sensor technologies (by revenue, data table)
2.1.What is a sensor?
2.2.Sensor value chain example: Digital camera
2.3.Photodetector working principles
2.4.Quantifying photodetector and image sensor performance
2.5.Extracting as much information as possible from light
2.6.Autonomous vehicles will need machine vision
2.7.Trends in autonomous vehicle adoption
2.8.What are the levels of automation in cars?
2.9.Global autonomous car market
2.10.How many camera needed in different automotive autonomy levels
2.11.Growing drone uses provides extensive market for emerging image sensors
2.12.Emerging image sensors required for drones
3.1.Market forecast methodology
3.2.Parametrizing forecast curves
3.3.Determining total addressable markets (TAMs)
3.4.Determining revenues
3.5.10-year short-wave infra-red (SWIR) image sensors market forecast: by volume
3.6.10-year hybrid OPD-on-CMOS image sensors market forecast: by volume
3.7.10-year hybrid OPD-on-CMOS image sensors market forecast: by revenue
3.8.10-year hybrid QD-on-CMOS image sensors market forecast: by volume
3.9.10-year hybrid QD-on-CMOS image sensors market forecast: by revenue
3.10.10-year thin film organic and perovskite photodetectors (OPDs and PPDs) market forecast: by volume
3.11.10-year thin film organic and perovskite photodetectors (OPDs and PPDs) market forecast: by revenue
3.12.10-year hyperspectral imaging market forecast: by volume
3.13.10-year hyperspectral imaging market forecast: by revenue
3.14.10-year event-based vision market forecast: by volume
3.15.10-year event-based vision market forecast: by revenue
3.16.10-year wavefront imaging market forecast: by volume
3.17.10-year wavefront imaging market forecast: by revenue
3.18.10-year flexible x-ray image sensors market forecast: by volume
4.1.Conventional image sensors: Market overview
4.2.Key components in a CMOS image sensor (CIS)
4.3.Sensor architectures: Front and backside illumination
4.4.Process flow for back-side-illuminated CMOS image sensors
4.5.Comparing CMOS and CCD image sensors
4.6.Benefits of global rather than rolling shutters
5.1.Motivation for short-wave infra-red (SWIR) imaging
5.2.SWIR imaging reduces light scattering
5.3.SWIR: Incumbent and emerging technology options
5.4.Applications for SWIR imaging
5.4.1.Applications for SWIR imaging
5.4.2.Identifying water content with SWIR imaging
5.4.3.SWIR for autonomous mobility
5.4.4.SWIR imaging enables better hazard detection
5.4.5.SWIR enables imaging through silicon wafers
5.4.6.Imaging temperature with SWIR light
5.4.7.Visualization of foreign materials during industrial inspection
5.4.8.Spectroscopic chemical sensors
5.4.9.SWIR image sensing for industrial process optimization
5.4.10.MULTIPLE (EU Project): Focus areas, targets and participants
5.4.11.SWIR spectroscopy: Wearable applications
5.4.12.SWIR spectroscopy: Determining water and body temperature via wearable technology
5.4.13.SWIR spectroscopy: Alcohol detection
5.4.14.SWIR image sensors for hyperspectral imaging
5.4.15.SWIR sensors: Application overview
5.4.16.SWIR application requirements
5.5.InGaAs sensors - existing technology for SWIR imaging
5.5.1.Existing long wavelength detection: InGaAs
5.5.2.The challenge of high resolution, low cost IR sensors
5.5.3.InGaAs sensor design: Solder bumps limit resolution
5.5.4.Sony improve InGaAs sensor resolution and spectral range
5.6.Emerging inorganic SWIR technologies and players
5.6.1.Trieye: Innovative silicon based SWIR sensors
5.6.2.OmniVision: Making silicon CMOS sensitive to NIR (II)
5.6.3.SWOT analysis: SWIR image sensors (non-hybrid, non-InGaAs)
5.6.4.Supplier overview: Emerging SWIR image sensors
5.6.5.Company profiles: SWIR imaging (excluding hybrid approaches)
6.1.OPD-on-CMOS hybrid image sensors
6.2.Hybrid organic/CMOS sensor
6.3.Hybrid organic/CMOS sensor for broadcast cameras
6.4.Comparing hybrid organic/CMOS sensor with backside illumination CMOS sensor
6.5.Progress in CMOS global shutter using silicon technology only
6.6.Fraunhofer FEP: SWIR OPD-on-CMOS sensors (I)
6.7.Fraunhofer FEP: SWIR OPD-on-CMOS sensors (II)
6.8.Academic research: Twisted bilayer graphene sensitive to longer wavelength IR light
6.9.Technology readiness level of OPD-on-CMOS detectors by application
6.10.SWOT analysis of OPD-on-CMOS image sensors
6.11.Supplier overview: OPD-on-CMOS hybrid image sensors
6.12.Company profiles: OPD-on-CMOS
7.1.Quantum dots as optical sensor materials
7.2.Lead sulphide as quantum dots
7.3.Quantum dots: Choice of the material system
7.4.Applications and challenges for quantum dots in image sensors
7.5.QD layer advantage in image sensor (I): Increasing sensor sensitivity and gain
7.6.QD-Si hybrid image sensors(II): Reducing thickness
7.7.Detectivity benchmarking (I)
7.8.Detectivity benchmarking (II)
7.9.Hybrid QD-on-CMOS with global shutter for SWIR imaging.
7.10.QD-Si hybrid image sensors: Enabling high resolution global shutter
7.11.QD-Si hybrid image sensors(IV): Low power and high sensitivity to structured light detection for machine vision?
7.12.How is the QD layer applied?
7.13.Advantage of solution processing: ease of integration with ROIC CMOS?
7.14.QD optical layer: Approaches to increase conductivity of QD films
7.15.Quantum dots: Covering the range from visible to near infrared
7.16.SWIR sensitivity of PbS QDs, Si, polymers, InGaAs, HgCdTe, etc...
7.17.Hybrid QD-on-CMOS image sensors: Processing
7.17.1.Value chain and production steps for QD-on-CMOS
7.17.2.Advantage of solution processing: Ease of integration with CMOS ROICs?
7.17.3.Quantum dot films: Processing challenges
7.17.4.QD-on-CMOS with graphene interlayer
7.17.5.Challenges for QD-Si technology for SWIR imaging
7.17.6.QD-on-CMOS sensors: Ongoing technical challenges
7.17.7.Technology readiness level of QD-on-CMOS detectors by application
7.18.Hybrid QD-on-CMOS image sensors: Key players
7.18.1.SWIR Vision Systems: Hybrid quantum dots for SWIR imaging
7.18.2.SWIR Vision Sensors: First commercial QD-CMOS cameras
7.18.3.IMEC: QD-on-CMOS integration examples (I)
7.18.4.IMEC: QD-on-CMOS integration examples (II)
7.18.5.RTI International: QD-on-CMOS integration examples
7.18.6.QD-on-CMOS integration examples (ICFO continued)
7.18.7.Emberion: QD-graphene SWIR sensor
7.18.8.Emberion: QD-Graphene-Si broadrange SWIR sensor
7.18.9.Emberion: VIS-SWIR camera with 400 to 2000 nm spectral range
7.18.10.Qurv Technologies: Graphene/quantum dot image sensor company spun off from ICFO
7.18.11.Academic research: QD-on-CMOS from Hanyang University (South Korea)
7.18.12.Academic research: Colloidal quantum dots enable mid-IR sensing
7.18.13.Academic research: Plasmonic nanocubes make a cheap SWIR camera
7.19.Summary: QD-on-CMOS image sensors
7.19.1.Summary: QD/OPD-on-CMOS detectors
7.19.2.SWOT analysis of QD-on-CMOS image sensors
7.19.3.Supplier overview: QD-on-CMOS hybrid image sensors
7.19.4.Company profiles: Hybrid QD-on-CMOS image sensors
8.1.Introduction to thin film photodetectors (organic and perovskite)
8.2.Organic photodetectors (OPDs)
8.3.Thin film photodetectors: Advantages and disadvantages
8.4.Reducing dark current to increase dynamic range
8.5.Tailoring the detection wavelength to specific applications
8.6.Extending OPDs to the NIR region: Use of cavities
8.7.Technical challenges for manufacturing thin film photodetectors from solution
8.8.Materials for thin film photodetectors
8.9.Thin film organic and perovskite photodiodes (OPDs and PPDs): Applications and key players
8.9.1.Applications of organic photodetectors
8.9.2.OPDs for biometric security
8.9.3.Spray-coated organic photodiodes for medical imaging
8.9.4.ISORG: 'Fingerprint on display' with OPDs
8.9.5.ISORG: Flexible OPD applications using TFT active matrix
8.9.6.ISORG: First OPD production line
8.9.7.Cambridge display technology: Pulse oximetry sensing with OPDs
8.9.8.Holst Center: Perovskite based image sensors
8.9.9.Commercial challenges for large-area OPD adoption
8.9.10.Technical requirements for thin film photodetector applications
8.9.11.Thin film OPD and PPD application requirements
8.9.12.Application assessment for thin film OPDs and PPDs
8.9.13.Technology readiness level of organic and perovskite photodetectors by applications
8.10.Organic and perovskite thin film photodetectors (OPDs and PPDs): Summary
8.10.1.Summary: Thin film organic and perovskite photodetectors
8.10.2.SWOT analysis of large area OPD image sensors
8.10.3.Supplier overview: Thin film photodetectors
8.10.4.Company profiles: Organic photodiodes (OPDs)
9.1.Introduction to hyperspectral imaging
9.2.Multiple methods to acquire a hyperspectral data-cube
9.3.Contrasting device architectures for hyperspectral data acquisition (II)
9.4.Line-scan (pushbroom) cameras ideal for conveyor belts and satellite images
9.5.Comparison between 'push-broom' and older hyperspectral imaging methods
9.6.Line-scan hyperspectral camera design
9.7.Snapshot hyperspectral imaging
9.8.Illumination for hyperspectral imaging
9.9.Pansharpening for multi/hyper-spectral image enhancement
9.10.Hyperspectral imaging as a development of multispectral imaging
9.11.Trade-offs between hyperspectral and multi spectral imaging
9.12.Towards broadband hyperspectral imaging
9.13.Hyperspectral imaging: Applications
9.13.1.Hyperspectral imaging and precision agriculture
9.13.2.Hyperspectral imaging from UAVs (drones)
9.13.3.Agricultural drones ecosystem develops
9.13.4.Satellite imaging with hyperspectral cameras
9.13.5.Historic drone investment creates demand for hyperspectral imaging
9.13.6.In-line inspection with hyperspectral imaging
9.13.7.Object identification with in-line hyperspectral imaging
9.13.8.Sorting objects for recycling with hyperspectral imaging
9.13.9.Food inspection with hyperspectral imaging
9.13.10.Hyperspectral imaging for skin diagnostics
9.13.11.Hyperspectral imaging application requirements
9.14.Hyperspectral imaging: Key players
9.14.1.Comparing hyperspectral camera manufacturers
9.14.2.Specim: Market leaders in line-scan imaging
9.14.3.Headwall Photonics
9.14.4.Cubert: Specialists in snapshot spectral imaging
9.14.5.Hyperspectral imaging wavelength ranges
9.14.6.Hyperspectral wavelength range vs spectral resolution
9.14.7.Hyperspectral camera parameter table
9.14.8.Companies analysing and applying hyperspectral imaging
9.14.9.Condi Food: Food quality monitoring with hyperspectral imaging
9.14.10.Orbital Sidekick: Hyperspectral imaging from satellites
9.14.11.Gamaya: Hyperspectral imaging for agricultural analysis
9.15.Summary: Hyperspectral imaging
9.15.1.Summary: Hyperspectral imaging
9.15.2.SWOT analysis: Hyperspectral imaging
9.15.3.Supplier overview: Hyperspectral imaging
9.15.4.Company profiles: Hyperspectral imaging
10.1.What is event-based sensing?
10.2.General event-based sensing: Pros and cons
10.3.What is event-based vision? (I)
10.4.What is event-based vision? (III)
10.5.What does event-based vision data look like?
10.6.Event-based vision: Pros and cons
10.7.Event-based vision sensors enable increased dynamic range
10.8.Cost of event-based vision sensors
10.9.Importance of software for event-based vision
10.10.Applications for event-based vision
10.10.1.Promising applications for event-based vision
10.10.2.Event-based vision for autonomous vehicles
10.10.3.Event-based vision for unmanned aerial vehicle (UAV) collision avoidance
10.10.4.Occupant tracking (fall detection) in smart buildings
10.10.5.Event-based vision for augmented/virtual reality
10.10.6.Event-based vision for optical alignment / beam profiling
10.10.7.Event-based vision application requirements
10.10.8.Technology readiness level of event-based vision by application
10.11.Event-based vision: Key players
10.11.1.Event-based vision: Company landscape
10.11.2.IniVation: Aiming for organic growth
10.11.3.Prophesee: Well-funded and targeting autonomous mobility
10.11.4.CelePixel: Focussing on hardware
10.11.5.Insightness: Vertically integrated model targeting UAV collision avoidance
10.12.Summary: Event-based vision
10.12.1.Summary: Event-based vision
10.12.2.SWOT analysis: Event-based vision
10.12.3.Supplier overview: Event-based vision
10.12.4.Company profiles: Event-based vision
11.1.Motivation for wavefront imaging
11.2.Conventional Shack-Hartman wavefront sensors
11.3.Phasics: Innovators in wavefront imaging
11.4.Wooptix: Light-field and wavefront imaging
11.5.Summary: Wavefront imaging
11.6.SWOT analysis: Wavefront imaging
11.7.Supplier overview: Wavefront imaging sensors
11.8.Company profiles: Wavefront imaging
12.1.Conventional x-ray sensing
12.2.Flexible image sensors based on amorphous-Si
12.3.Spray-coated organic photodiodes for medical imaging
12.4.Direct x-ray sensing with organic semiconductors
12.5.Holst Center develop perovskite-based x-ray sensors (I)
12.6.Holst Center develop perovskite-based x-ray sensors (II)
12.7.Technology readiness level of flexible and direct x-ray sensors
12.8.Summary: Flexible and direct x-ray image sensors
12.9.SWOT analysis: Flexible and direct x-ray image sensors
12.10.Supplier overview: Flexible x-ray image sensors
12.11.Company profiles: Flexible and direct x-ray image sensors

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