最先端イメージセンサー技術 2024-2034年: アプリケーション、市場

（詳細は目次のページでご確認ください）

1. 全体概要

2. イントロダクション

3. 市場予測

4. 既存可視画像式センサー（CCDとCMOS）概要

5. 短波長赤外光（SWIR）イメージセンサー（InGaAs、拡張性シリコン、その他の無機材料)

6. ハイブリッドOPD-on-CMOSイメージセンサー（SWIR用を含む）

7. ハイブリッドQD-on-CMOSイメージセンサー（SWIR用を含む）

8. 薄膜光検出器（有機およびペロブスカイト）

9. ハイパースペクトルイメージング

10. 超小型化分光器

11. イベント駆動ビジョン

12. 波面（位相）イメージング

13. フレキシブルX線イメージセンサー

14. 量子イメージセンサー

15. コンピュータビジョン

 

 

IDTechEx's "Emerging Image Sensor Technologies 2024-2034: Applications and Markets" report evaluates the most diverse range of image sensors with varying resolutions and wavelength sensitivities. This technology is set to impact multiple industries from healthcare, biometrics, autonomous driving, agriculture, chemical sensing, and food inspection, among several others. The growing importance of these technologies is expected to contribute towards the growth of this market, with projections of it reaching US$739 million by 2034. This figure is, in fact, conservative because a much larger market value is predicted if these sensors take-off I the consumer electronics sector. As an example, the QD-on-CMOS market would note a 25X increase in revenue value if the consumer electronics space is considered.

 

Primary insight from interviews with individual players, ranging from established players to innovative start-ups, is included alongside 25 detailed company profiles that include discussion of both technology and business models and SWOT analysis. Additionally, the report includes technological and commercial readiness assessments, split by technology and application. It also discusses the commercial motivation for developing and adopting each of the emerging image sensing technologies and evaluates the barriers to entry.

 

 

Fundamental topics are covered throughout this report including the evaluation of individual technology readiness levels as well as detailed SWOT analyses for each technology.

 

From these insights it is possible to predict which technologies are most likely to succeed and which companies have positioned themselves in a more competitive position to thrive in the market.

 

This report also covers different applications that will benefit from these technologies as well as key challenges they may face in commercializing their products. The rate at which autonomy develops, for instance, will be partly dependent on the maturity of these sensors in the medium to long-term. Increased sensor maturity is synonymous with more cost effective and advanced technology, i.e., more sensitive sensors.

 

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.

 

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 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.

 

There are several competitive emerging SWIR technologies. These include hybrid image sensors where an additional light absorbing thin film layer made of organic semiconductors or quantum dots is placed on top of a CMOS read-out circuit to increase the wavelength detection range into the SWIR region. Another technology is extended-range silicon where the properties of silicon are modified to extend the absorption range beyond its bandgap limitations. 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.

 

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 that can exceed US$50,000. Emerging technologies using silicon or thin film materials look set to disrupt both these aspects, with snapshot imaging offering an alternative to line-scan cameras and with the new SWIR sensing technologies method facilitating cost reduction and adoption for a wider range of applications.

 

Another emerging image sensing technology is event-based vision, also known as dynamic vision sensing (DVS). 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 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.

 

The report also looks at the burgeoning market of miniaturized spectrometers. Driven by the growth in smart electronics and Internet of Things devices, low-cost miniaturized spectrometers are becoming increasingly relevant across different sectors. The complexity and functionalization of standard visible light sensors can be significantly improved through the integration of miniaturized spectrometers that can detect from the visible to the SWIR region of the spectrum. The future being imagined by researchers at Fraunhofer is a spectrometer weighing just 1 gram and costing a single dollar. Miniaturized spectrometers are expected to deliver inexpensive solutions to improve autonomous efficiency, particularly within industrial imaging and inspection as well as consumer electronics.

 

IDTechEx has 20 years of expertise covering emerging technologies, including image sensors, thin film materials, and semiconductors. Our analysts have closely followed the latest developments in relevant markets, interviewed key players within the industry, attended conferences, and delivered consulting projects on the field. This report examines the current status and latest trends in technology performance, supply chain, manufacturing know-how, and application development progress. It also identifies the key challenges, competition and innovation opportunities within the image sensor market.

 

This report provides the following information:

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