Archives for May 2019

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Autosens publishes a number of the interviews from its conference held in Detroin in May this year:

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Arxiv.org paper "Video from Stills: Lensless Imaging with Rolling Shutter" by Nick Antipa, Patrick Oare, Emrah Bostan, Ren Ng, and Laura Waller from UCB pulls a high speed 4,500fps video from a rolling shutter stills sensor:

"Because image sensor chips have a finite bandwidth with which to read out pixels, recording video typically requires a trade-off between frame rate and pixel count. Compressed sensing techniques can circumvent this trade-off by assuming that the image is compressible. Here, we propose using multiplexing optics to spatially compress the scene, enabling information about the whole scene to be sampled from a row of sensor pixels, which can be read off quickly via a rolling shutter CMOS sensor. Conveniently, such multiplexing can be achieved with a simple lensless, diffuser-based imaging system. Using sparse recovery methods, we are able to recover 140 video frames at over 4,500 frames per second, all from a single captured image with a rolling shutter sensor. Our proof-of-concept system uses easily-fabricated diffusers paired with an off-the-shelf sensor. The resulting prototype enables compressive encoding of high frame rate video into a single rolling shutter exposure, and exceeds the sampling-limited performance of an equivalent global shutter system for sufficiently sparse objects."

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Taiwan branch of Daiwa Capital Markets publishes report "The more the merrier: multi-cameras are the next mega trend in smartphones" dated by Oct. 2018. Few charts and tables form the report:


Triple Cameras Technology challenges, according to Daiwa:

• Calibration. The cameras need to be carefully calibrated for optical properties of a triple aperture system. The calibration consists of recovering camera parameters, location and orientation for external parameters and focal lengths for internal parameters. In addition, the calibration should be completed in the camera manufacturing line for numerous tests on any dynamic physical changes, such as temperature variances and drop test.
• Firmware/algorithm. A triple-cam system demands more complexity on the firmware side as the framework in a triple-cam system will have to deal with each of the 3 cameras to operate as one integrated camera. In addition, algorithms would have the same challenges to ensure reasonable processing run time and also maintain zero distortion of the image quality from multiple inputs.
• Power consumption. The complex algorithm and firmware, which require more processing power in power management, frame request and memory management, could lead to higher power consumption for the entire camera system (camera + processing platform).

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Renato Turchetta, CEO of Imasenic, presented "Smart CMOS Image Sensors" at Workshop on the Architecture of Smart Cameras, held on 27-28 June 2018, Coimbra, Portugal. Below are just few slides from the 57-page presentation:

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Greenthread LLC files patent infringement lawsuit against Samsung:

"The Greenthread Patents cover foundational semiconductor technologies in the design and manufacture of integrated circuits such as memory, including but not limited to DRAM and NAND flash, and image sensors. Specifically, the Greenthread Patents describe semiconductor devices that employ graded dopants and well regions for, e.g., creating electric fields for aiding and/or retarding the movement of carriers to and/or from the semiconductor surface to/from the semiconductor substrate."

Few patents mentioned in the filing list G. R. Mohan Rao as an inventor:

US8421195B2 “Semiconductor Devices with Graded Dopant Regions”
US8106481B2 “Semiconductor Devices with Graded Dopant Regions”
US9647070B2 “Semiconductor Devices with Graded Dopant Regions”

The filing has an interesting info about Samsung S5K2X7SP sensor that allegedly infringes on the '070 patent above. This is fairly modern 24MP 0.9um Tetracell sensor featuring DTI, vertical transfer gate (VTG), and many other recent innovations from Samsung:

"Upon information and belief, the Accused Image Sensor Instrumentalities are in relevant part substantially similar to the exemplary S5K2X7SP CMOS image sensor, in particular with regard to the manner in which the Accused Image Sensor Instrumentalities include and utilize regions with graded dopant concentrations. Upon information and belief, Samsung fabricates the Accused Image Sensor Instrumentalities according to processes that utilize substantially similar process steps, including process steps for creating regions with graded dopant concentrations."

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VentureBeat, Reuters, Globes, TechTime: Israel-based SWIR image sensor startup TriEye announces a $17M financing round led by Intel Capital. The new funds will be used to develop its sensors for low-visibility situations for connected and autonomous vehicles.

TriEye's SWIR technology is CMOS-based, enabling the scalable mass-production of SWIR sensors and reducing the cost by a factor of 1,000x compared to InGaAs-based technology. The initial SWIR camera samples are expected to enter the market in 2020.

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IFNews, Zol, GizChina: Reportedly, the upcoming Vivo NEX 2 smartphone will use a concept of detachable camera - it can be attached either in pre-determined spots on the smartphone, or, according to other sources, in any spot. So, a single camera can be used in both front and rear configurations:

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Celepixel demos its event-based sensors in different conditions:





More video examples can be found in the company's 2016 video.

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IEEE Spectrum publishes Ams article "3 Types of 3D Sensing for Smartphones and Self-Driving Cars" by Markus Rossi, VP for innovations in optical sensors. Few quotes:

"Structured light cameras project patterns of light onto objects and analyze distortions in the reflection. Time-of-flight sensors measure the time it takes for light to travel to and from an object as a way to discern its distance. And active stereo vision combines two or more types of near-infrared cameras to triangulate the location and movements of an object.

Facial recognition is today the primary application of 3D sensing... But 3D sensing is also becoming important in the automotive sector, both outside the vehicle as well as inside.

In general, detecting reflected light (from dots or flood illumination) at close distance (say, 40 to 60 cm) is not a major challenge for current systems. And these systems are defined to work in a number of different scenarios. The most critical one is outdoors—the intensity of the reflected dots needs to be strong enough that it can be detected when the light sensor is also being flooded with sunlight.

Calibration is very important for all three technologies—both factory calibration as well as real-time calibration... For example, in order to do triangulation through stereo vision, the cameras need to be positioned at a certain base line (typically in the range of 25 mm). This distance needs to stay constant down to a micron or a fraction of a micron apart. A mechanical shock or a slight variation in temperature could affect their performance."

Infineon: The Embedded Vision Alliance awarded Infineon 3D ToF single chip solution as “Product of the Year” in the category “Sensors.”

“Congratulations to Infineon for winning Best Sensor in our 2019 Vision Product of the Year awards,” said Jeff Bier, Founder of the Embedded Vision Alliance. “Innovative sensor designs, like Infineon's 3D Image Sensor, are enabling new 3D vision experiences such as smartphone face recognition and augmented reality. Infineon has been a pioneer in creating 3D image sensors for visual AI applications. I applaud the company’s ongoing investment in innovation and quality in this rapidly changing and often confusing market. I’m proud that Infineon is a member of the Embedded Vision Alliance and delighted they have been recognized with this outstanding honor.”

“We would like to thank the Embedded Vision Alliance for having honored us with the ‘Product of the Year’ Award, which recognizes Infineon’s innovation and performance of our REAL3™ ToF-based 3D imager the category ‘Sensors’, said Philipp von Schierstaedt, VP, RF & Sensors, Infineon. “Building upon the combined expertise of Infineon and pmdtechnologies, the novel sensor delivers a new level of 3D camera capabilities in mobile device applications. Infineon’s REAL3TM ToF sensor enables a unique user’s experience in secure face-authentication, computational photography and seamless AR-applications.”


Digitimes: Etron started shipping production version of its 3D stereo processors and expects the shipments to top 50,000 units in May.

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Shimonomura Lab from Ritsumeikan University, Japan, publishes a couple of nice videos complementing the previously published papers.

Video illustrating Light-In-Flight paper:



It could be a good market for DVS sensors, if one places such sensor under each button in a keyboard:



I'm not sure how large is the demand for this sensor, but looks like a fresh idea:

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IFNews publishes UBS cost analysis of Samsung Galaxy S10, S10+ and S10 e smartphones with cameras accounting for 14-16% of the BOM:


Samsung Galaxy S10 5G also features front and rear ToF cameras:

"S10 5G comes with total 4 cameras on the rear side. In addition to the same three cameras we have for S10+/S10 (12MP wide + 12MP telephoto + 16MP ultra-wide camera), the 5G model adds a Time-of-Flight module. For the ToF we think the sensor likely comes from Sony, and the module should also include one VCSEL laser which we believe is supplied by either Osram or ams AG. Interestingly for the rear side ToF, we think Sunny Optical could be the main supplier for the module, while SEMCO and Samsung are still doing the modules for the existing three cameras as well as for the combining of the final quad cam. We think the cost for the quad camera for S10 5G model could be c. $55, of which the ToF module alone could cost c. $10."


"On the front side, S10 5G also has dual front camera similar to that of S10+. One difference though is that it replaces the 8MP RGB depth camera with a Time-of-Flight. We think the ToF module structure is similar to that on the rear side: Sony likely supplying the ToF sensor, and one VCSEL laser likely supplied by Osram. We think Partron is the main module assembler for the ToF module, as well as for the combined dual camera module. We think the cost for the dual front camera for S10 5G model is c. $20, of which the ToF module alone could cost c. $10."

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ResearchInChina report "China Passenger Car Camera Market Report, 2019Q1" says:

In China, front view monocular camera is the one largely demanded in passenger car market, with its installations in 2019Q1 surging by 71.7% from the same period of 2018, according to our recent report -- China Passenger Car Camera Market Report, 2019Q1.

The top three players Aptiv, Bosch and Denso commanded 21.3%, 16.9% and 13.0% of the market, respectively.


PRNewswire: The automotive camera market is anticipated to hike from USD 5.5 billion in 2018 to around USD 19 billion by 2025 when the global industry shipments will register 17% growth, according to a 2019 Global Market Insights, Inc. report.

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PRNewswire: "Analysts predict that 20% of embedded notebook cameras will remain at VGA resolutions for the foreseeable future, due to cost considerations in the entry-level market," said Jason Chiang, product marketing manager at OmniVision. "By introducing this new OV0VA10 SoC with advanced pixel technology, OmniVision is increasing the performance and extending the viability of the VGA notebook camera market."

OmniVision OV0VA10 SoC integrates a VGA image sensor and signal processor in a single chip-scale package. The SoC's OmniPixel 3-HS allows entry level, thin-bezel notebook designers to incorporate the VGA camera with excellent low light image capture for applications such as videoconferencing. Additionally, it offers 30% lower power consumption than the leading competitor.

Additionally, its OmniPixel 3-HS enhances color performance with symmetric pixel design to eliminate color shading and optimize the signal-to-noise ratio. This SoC's integrated image sensor has a 1/10" optical format and 2.2um pixel size, enabling a 4mm camera module in the Y dimension for the latest entry level notebooks with thinner bezels. Additionally, it is manufactured using 200mm wafer process and is offered in an 8" chip-scale package with a DVP interface.

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FT discusses the US ban on components sale to Huawei. As Panasonic and Toshiba halt shipping their chips to the Chinese company, a question arises about Sony image sensors. They are used in all Huawei flagship phones and are difficult to substitute.

When contacted, Sony executives declined to comment on that. Jefferies analyst Atul Goyal estimates that Sony image sensor sales to Huawei is about 15% of the company's total CIS production. Oppo and Vivo use Sony sensors too, so if Huawei loses its market share to them, it should not impact Sony much. However, if Samsung takes over Huawei's products, Sony sales might suffer.

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Techcrunch, Wired, Forbes-1, Forbes-2: Aurora, an AI self-driving startup that recently raised $530M in Round B, acquires Montana-based 10 year-old FMCW Doppler LiDAR developer Blackmore. The 70 Blackmore employees will join the 250 Aurora's engineers and technicians. So far, Blackmore has raises $21.5M in tow financing rounds.

“We're really the pioneers in FM lidar,” Blackmore founder and CEO Randy Reibel says. “The easiest analogy is AM radio versus FM radio. If you think back to the days of broadcast radio, AM radio has a lot of interference. It’s staticky. When you work with an FM lidar system, or an FM radio, you don’t have that interference. That’s a huge deal in this space, especially from a safety perspective. … As we start to pack the roads with hundreds and hundreds of lidars in close proximity, they’re going to crosstalk. With an FM lidar, you don’t have any of those interference challenges.”

That kind of consolidation will likely continue, Reibel predicted, in part because it’s challenging for LiDAR companies to “go it alone.” AV companies are particularly protective of their tech and opening the door to an outside LiDAR company takes convincing.

Aurora said it is not interested in manufacturing hardware, whether it’s cars or LiDARs. The company will work with automotive Tier 1 suppliers and other partners as it scales.

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From Deloitte doc: "In 2018, Shanghai Will Semiconductor acquired a 85% stake in Beijing SuperPix Micro Technology and a 96% stake in Beijing OmniVision Technologies for USD2.18bn to take advantage of the high-end technics of OmniVision and cost control capabilities of SuperPix."

See here the previous reports on that.

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Korea-based Yanta Research publishes its view on CIS market shares with Hynix taking #4 spot on the chart:

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PRNewswire: OmniVision announces the OVM7251 VGA CameraCubeChip module built on the 3um OmniPixel 3-GS global shutter pixel. The module is available in an 850nm version for AR/VR eye tracking, and a 940nm version for machine vision and 3D sensing in mobile facial authentication.

“Until now, most camera modules for these applications have been built with rolling shutters, which have latency issues. Meanwhile, global shutter modules have previously been too large and expensive,” said Aaron Chiang, marketing director at OmniVision. “Our new OVM7251 overcomes these challenges by providing a cost effective VGA module with global shutter performance in a wafer-level, reflowable form factor.”

The OVM7251’s sleep current consumption is 5mA, and during active mode. The module is available now for sampling and volume production, along with an evaluation kit.

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Pixart and National Chiao Tung University, Taiwan publish an open access paper "Fast Imaging in the Dark by using Convolutional Network" by Mian Jhong Chiu, Guo-Zhen Wang, and Jen-Hui Chuang presented at 2019 IEEE International Symposium on Circuits and Systems (ISCAS):

"While fast imaging in low-light condition is crucial for surveillance and robot applications, it is still a formidable challenge to resolve the seemingly inevitable high noise level and low photon count issues. A variety of image enhancement methods such as de-blurring and de-noising have been proposed in the past. However, limitations can still be found in these methods under extreme low-light condition. To overcome such difficulty, a learning-based image enhancement approach is proposed in this paper. In order to support the development of learning-based methodology, we collected a new low-lighting dataset (less than 0.1 lux) of raw short-exposure (6.67 ms) images, as well as the corresponding long-exposure reference images. Based on such dataset, we develop a light-weight convolutional network structure which is involved with fewer parameters and has lower computation cost compared with a regular-size network. The presented work is expected to make possible the implementation of more advanced edge devices, and their applications."

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Sony posts the following message at LinkedIn: "Sony Semiconductor Solutions, the largest image sensor supplier in the world, is establishing new design centers across Europe (Norway, Germany, Switzerland).

The design centers will focus on the design and the development of image sensors targeting a plethora of markets including mobile, automotive and industrial. The European design teams will work closely with Sony Semiconductor design and manufacturing teams in Japan.

We are starting a wide recruitment campaign all over Europe with the purpose of hiring analogue and digital engineers as well as engineering managers and field application engineers."

Thanks to AB for the pointer!

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As noted in comments to my previous post on Heriot Watt University Ge-on-Si SPADs, there is a similar work in Eduardo Charbon group in EPFL and Delft University. The most recent development is presented in open-access IEEE TED paper "CMOS-Compatible PureGaB Ge-on-Si APD Pixel Arrays" by Amir Sammak, Mahdi Aminian, Lis K. Nanver, and Edoardo Charbon:

"Pure gallium and pure boron (PureGaB) Ge-on-Si photodiodes were fabricated in a CMOS compatible process and operated in linear and avalanche mode. Three different pixel geometries with very different area-to-perimeter ratios were investigated in linear arrays of 300 pixels with each a size of 26 x 26 mu m(2). The processing of anode contacts at the anode perimeters leaving oxide covered PureGaB-only light-entrance windows, created perimeter defects that increased the vertical Ge volume but did not deteriorate the diode ideality. The dark current at 1 V reverse bias was below 35 mu A/cm(2) at room temperature and below the measurement limit of 2.5 x 10(-2) mu A/cm(2) at 77 K. Spread in dark current levels and optical gain, that reached the range of 10(6) at 77 K, was lowest for the devices with largest perimeter. All device types were reliably operational in a wide temperature range from 77 K to room temperature. The spectral sensitivity of the detectors extended from visible to the telecom band with responsivities of 0.15 and 0.135 A/W at 850 and 940 nm, respectively."


Leeds University publishes a PhD Thesis "Electronic Transport Properties of Silicon-Germanium Single Photon Avalanche Detectors" by Helen Rafferty.

"Single photon avalanche detectors (SPADs) have uses in a number of applications, including time-of-flight ranging, quantum key distribution and low-light sensing. Germanium has an absorption edge at the key communications wavelengths of 1.3-1.55um, and can be grown epitaxially on silicon, however, SiGe SPADs exhibit a number of performance limitations, including low detection efficiencies, high dark counts and afterpulsing. Unintentional doping may affect electronic performance, and band-to-band tunnelling at high operational voltages SPADs may lead to noise currents. Additionally, defects in the Si/Ge interface lead to trap states within the bandgap and contribute to afterpulsing.

This work investigates a range of critical performance parameters in SiGe SPADs. The effect of intentional and unintentional doping in SPADs on electric fields, potential profiles and carrier transport in the device is investigated, and optimal dopant profiles for a SiGe SPAD discussed. The dependence of band-to-band tunnelling currents in Ge on bias voltage, Ge thickness and temperature is investigated, and these currents are compared to other sources of noise currents in SPADs. DFT calculations of misfit dislocation structures in Ge are undertaken, to establish electronic bandstructures and optimised geometries for these defects, and identify trap states in the bandgap, which may contribute to afterpulsing and dark counts in SPADs. A number of directions for continuing work are identified, to progress understanding of noise currents and afterpulsing in SPADs."

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GlobeNewsWire: Himax and its wholly-owned subsidiary, Emza Visual Sense announce WiseEye 2.0 NB, an intelligent vision solution for notebook computers. It is said to be the industry’s first ultra-low power, AI-based intelligent visual sensor that adds the advanced human presence awareness functionality for notebooks while supporting always-on operation. The solution has been tested and well received by the leading global chipmaker and Quanta Computer, the world’s largest original design manufacturer (ODM) of notebook computers, for inclusion in their next-generation mainstream notebook platforms.

"We are excited that the WiseEye 2.0 solution’s coverage has expanded from IoT devices to notebook computers as it opens up new growth opportunities in the high-end notebook ecosystem. It’s a real win-win situation for OEMs, ODMs and Himax/Emza. Our unique technology consists of Himax’s CMOS image sensor and Emza’s AI-based computer vision algorithm running on an Himax-designed ASIC, all catered for ultra-low power consumption to enable always-on operation of the end device. The partnership with the world’s leading chipmaker and notebook ODM allows us to closely engage with multiple global notebook OEMs, targeting their next generation product launches for the 2020 back to school season,” said Jordan Wu, President and Chief Executive Officer of Himax Technologies.

The key features of the WiseEye 2.0 NB intelligent visual sensor for human presence detection include:

• Enhanced AI-enabled User Experience: a combination of ultra-low power image sensor and energy efficient CV image processing algorithm, augmented with AI-based machine learning, enable automatic wake up of the notebook from standby mode or locking the screen based on specific human behavior or movements. This is a significant improvement over the existing solutions that do not function when the notebook is in sleep mode.
• Extended Battery Life: AI-based always-on camera (AoS) can detect user engagement levels based on presence and face posing, enabling power management of the display and maximizing battery life.
• Improved Privacy and Security: WiseEye 2.0 NB can detect the presence of additional humans in the field of view and send an alert to the user.
• Expanded FOV: Optimized for 60-90 deg. HFOV and flexible VFOV as opposed to currently used simple sensors which are sensitive to screen angles. Wider FOV can enable early detection and sensing of flexible movement even when users are close to the screen.
• Increased Distance Detection: High accuracy sensing of human presence from up to 5 meters away enables a quick response to user detection even when approaching the device at high speeds.
• Production Friendly Technology: Does not need strict tolerances in mounting versus solutions that require calibration due to limited FOV.
• Tiny Form Factor: The Himax 2-in-1 (AoS and RGB) sensor is the first hybrid CMOS sensor specifically for notebook computers. The sensor combines high quality HD image capabilities with ultra-low-power visual sensing, for AI context awareness applications. The new CMOS sensor will be available at end of 2019
• Privacy Awareness: The sensor image is processed entirely on the dedicated WiseEye 2.0 processor, co-located with the CMOS image sensor, so that the image is never transmitted to the main platform. This architecture is specially designed to meet the highest privacy standards.

“Expanding our industry leading intelligent vision solutions into notebook computing is a great achievement,” said Yoram Zylberberg, CEO of Emza Visual Sense, “applying ultra low-power machine learning AI for notebooks is the key especially while device operation is suspended to extend the life of the battery. Leveraging the AI benefits that we developed for IoT and now applying it to notebook is a great demonstration of the agility of our solution and our readiness to adapt the technology for specific customer requirements.”

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ON Semi demos its Hayabusa super-exposure technology for automotive HDR with LFM:

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Sony held a corporate strategy meeting FY19 and set the directions for its CIS business:

• In imaging, Sony was able to deliver a stable supply of high value-added product to a market that is evolving not only toward higher resolution, but also toward multiple sensors per camera and larger sized sensors, while, at the same time, maintaining its number one market share position in CMOS sensors on a revenue basis.
• Achieved steady development in the automotive and sensing parts of the business.
• We expect to leverage the superior technology Sony has developed in this business to maintain our industry-leading position going forward.
• Approximately 80% of CMOS sensor sales are to smartphones. Although this market has matured, demand for sensors continues to grow due to adoption of multiple sensors and larger sized sensors in smartphones. Demand for Time-of-Flight sensors in smartphones is also expected to increase.
• Although investment in greater production capacity over the next few years is necessary, CMOS sensor production capacity does not become obsolete, resulting in high return on investment in the long term.
• Initiatives in long-term growth prospects such as automotive sensors and Edge AI.
• Expand business through fields such as distance measurement and automotive. Sony's automotive sensors are receiving positive external feedback.
• Stacked CMOS image sensors to be made more intelligent by embedding AI functionality to the logic layer.
• Sony will also actively pursue alliances with partner companies. Recently announced an MOU with Microsoft to collaborate in the area of AI.

Also, Sony 2019 IR Day updates on the company's imaging business directions:

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BusinessWire: Magik Eye reveals Invertible Light, a new method for depth sensing that is said to enable the smallest, fastest and most power-efficient 3D sensing. “While Structured Light, Time of Flight and Stereo Scoping Imaging are the primary methods today, Invertible Light aims to transform 3D sensing in the coming age of robotics and machine vision for the masses,” said Takeo Miyazawa, Founder & CEO of Magik Eye.

Current methods such as Structured Light have been around for more than 25 years and are based on legacy design. They fundamentally require the projection of a specific or random pattern to measure the distance to an object in 3D. The result is significant power usage, multiple components and complexity for production. All of this ultimately translates into higher cost for the consumer. Invertible Light by contrast projects a regular dot pattern on an object using only a projector and an image sensor. The result of this breakthrough in optics and mathematics is the smallest, fastest & most power-efficient 3D sensing.

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PRNewswire: OmniVision announced the OV5678 said to be the industry’s first 5MP RGB-IR image sensor for 2-in-1 convertible laptops. This sensor enables a single camera with greater accuracy for IR Windows Hello facial authentication as well as RGB images for selfies and videoconferencing.

“Previously, Windows Hello facial authentication was not commonly found in 2-in-1 convertible laptops, as it required a second camera for IR functionality,” said Jason Chiang, product marketing manager at OmniVision. “The OV5678 eliminates the need for a second camera by combining RGB and IR capabilities in a single 5MP sensor, saving space while increasing value.”

To ensure high quality color images, the OV5678 is built on OmniVision’s 1.12 micron PureCel Plus pixel architecture with deep trench isolation for greatly reduced color crosstalk. Additionally, its buried color filter array (BCFA) has a high tolerance for collecting light with various incident light angles.

The PureCel Plus architecture also utilizes thicker silicon to improve QE when capturing images using NIR light outside the visible spectrum. This is accomplished with only 1.3MP, which is a quarter of the OV5678 sensor’s full resolution. This IR performance enables machine vision applications such as Windows Hello facial authentication. It can also be used to perform eye tracking for reduced power consumption when the user is not viewing the screen. Eye tracking can also enable user warnings about eye fatigue from looking at the screen for an extended period of time.

The OV5678 is available now for samples and volume production

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Gerald Buller leading Single-Photon Group in Heriot Watt University, UK, presents Ge-on-Si SPAD devices which are supposed to solve the low IR detection efficiency problems of the regular Si-based SPADs:


Recently, the group has published a Nature paper "High performance planar germanium-on-silicon single-photon avalanche diode detectors" by Peter Vines, Kateryna Kuzmenko, Jarosław Kirdoda, Derek C. S. Dumas, Muhammad M. Mirza, Ross W. Millar, Douglas J. Paul, and Gerald S. Buller:

"In the short-wave infrared, semiconductor-based single-photon detectors typically exhibit relatively poor performance compared with all-silicon devices operating at shorter wavelengths. Here we show a new generation of planar germanium-on-silicon (Ge-on-Si) single-photon avalanche diode (SPAD) detectors for short-wave infrared operation. This planar geometry has enabled a significant step-change in performance, demonstrating single-photon detection efficiency of 38% at 125 K at a wavelength of 1310 nm, and a fifty-fold improvement in noise equivalent power compared with optimised mesa geometry SPADs. In comparison with InGaAs/InP devices, Ge-on-Si SPADs exhibit considerably reduced afterpulsing effects. These results, utilising the inexpensive Ge-on-Si platform, provide a route towards large arrays of efficient, high data rate Ge-on-Si SPADs for use in eye-safe automotive LIDAR and future quantum technology applications."

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InVision: Emberion publishes an article on its graphene-based image sensors:

"The key features of the graphene-based VIS-SWIR sensor technology are the wide spectral response range, excellent sensitivity and noise performance, and large dynamic operation range. The spectral response range starts from 400nm and extends initially up to 1,800nm, in the future extending to even longer wavelengths. The properties of graphene, namely the high mobility of the charge carriers and the maximal surface to volume ratio of a 2D material, result into a low noise, high internal gain and non-saturating response behavior. The linear and full dynamic operation ranges are 60 and 120dB, respectively. The light absorbing materials pose a trade-off between the operation speed and sensitivity. Currently, the frame rate is limited to 100fps but is expected to increase in future products. In respect to Specific Detectivity (D*) and Noise Equivalent Irradiance (NEI) performance, these novel sensors are on par with InGaAs photodiodes in SWIR and outperform them in VIS spectral domain at 30fps operation speed. The graphene-based sensors can be operated in room temperature but the optimal performance is achieved with a one-stage Peltier cooling element. The sensors will offer cost-wise an attractive alternative to InGaAs sensors. Therefore, this new sensor technology will allow product concepts and applications which have previously been prohibited by the high cost of SWIR sensors and which require a wider spectral response range."

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