Archives for September 2017

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TU Delft and Harvest Imaging publish an open access paper "Temperature Sensors Integrated into a CMOS Image Sensor" by Accel Abarca, Shuang Xie, Jules Markenhof, and Albert Theuwissen. Apparently, the paper is a continuation of the MSc thesis "Integrating a Temperature Sensor into a CMOS Image Sensor."

"The test image sensor consists of pixels and temperature sensors pixels (=Tixels). The size of the Tixels is 11 μm × 11 μm. Pixels and Tixels are placed next to each other in the active imaging array and use the same readout circuits. The design and the first measurements of the combined image-temperature sensor are presented."

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Researches from University of Utah at Salt Lake City proved that AI can be somewhat successful in distinguishing between the digits as seen by image sensor with no lens. "Lensless-camera based machine learning for image classification" paper by Ganghun Kim, Stefan Kapetanovic, Rachael Palmer, and Rajesh Menon is published by arxiv.org. From the abstract:

"Finally, we demonstrated that the trained ML algorithm is able to classify the digits with accuracy as high as 99% for 2 digits. Our approach clearly demonstrates the potential for non-human cameras in machine-based decision-making scenarios."

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SEMI European Imaging & Sensors Summit 2017 is to be held on September 20-22 in Grenoble, France. The summit agenda has many interesting presentations:

• Sensor as a solution
  Chae Lee, Senior VP, LG Electonics
• In the era of mixed reality and augmented environment sensing, what does innovation mean? What is Leti’s vision?
  Marie Noelle Semeria, CEO, Cea-Leti
• 3D Time-of-Flight Cameras in Industrial Applications
  Thomas Kuhnke, Senior Electronics Design Engineer, 3D Business, Basler AG
• Hybrid semiconductor direct conversion CMOS x-ray imaging detectors with application examples
  Tuomas Pantsar, Chief Technology Officer / Charge Integration, Oy Ajat
• CMOS based microdisplays, imager and sensors enhanced by OLED/OPD integration
  Philipp Wartenberg, IC Design Engineer and Project Manager / Deputy head of department IC and System Design, Fraunhofer Institute for Organic Electronics, Electron Beam and Plasma Technology FEP
• TSMC Imaging Technology to Enable Innovation in AR/VR Applications
  Tripti Bhanti, Senior Technical Manager, TSMC Europe BV
• Fusion Bonding Enabler for Backside Illuminated Image Sensors - What’s Next
  Thomas Uhrmann, Director of Business Development, EVG
• Technological Trends in Thermal Image Sensors
  Christel-loic Tisse, Technical & Innovation Director, ULIS
• Packaging of Image Sensors and Devices
  Lutz Mattheier, Manager Assembly Technology Development, First Sensor Microelectronic Packaging GmbH
• Image Fusion: How to Best Utilize Dual Cameras
  Roy Fridman, Director of Product Marketing, Corephotonics
• High Performance CMOS Integrated Graphene Photodetectors
  Tapani Ryhänen, CEO, Emberion
• CMOS image sensor for bio-medical and scientific applications
  Renato Turchetta, CEO, IMASENIC
• New developments in logarithmic pixels and sensors
  Yang Ni, Founder & CTO, NIT
• Colour X-ray Imaging Solutions for Non-Destructive Testing based on Photon Counting Technology
  Juha Kalliopuska, Chief Executive Officer and Co-founder, ADVACAM
• High resolution global shutter image sensors for machine vision and 8K video
  Guy Meynants, Engineering Fellow, ams/CMOSIS
• VTT’s hyperspectral imaging technology - from high-performance applications towards volume scalability
  Anna Rissanen, Research Team Leader, VTT
• Image Sensors for Future VR
  Yiwan Wong, Partnership Lead, Oculus Research
• Ambient Sensing – New ways how intelligent devices can cope with the environment
  Roland Helm, Segment Head Sensors, Infineon Technologies AG

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Leti announces readiness of the new technology of very high-resolution (>1000 dpi) fingerprint sensor based on a matrix of interconnected piezoelectric ZnO nanowires:

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Sony announces Venice - the next generation digital cinema camera featuring 4K full-frame sensor capable of 60fps or 6K at 30fps speed. Venice is said to have a high speed readout which minimizes the jello effects typical in the CMOS sensors. The new sensor DR is promised to achieve 15+ stops.

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Recently, automotive LiDAR startups attract a lot of VC money:

LeddarTech announces a combined investment of US$101m led by Osram and including Delphi, Magneti Marelli and Integrated Device Technology as strategic investors, as well as Fonds de solidarité FTQ. This round of funding will allow LeddarTech to enhance its ASIC development efforts, expand its R&D team, and accelerate ongoing LiDAR development programs with select Tier-1 automotive customers for rapid market deployment.

“We are thrilled to welcome such an influential group of companies as strategic investors, with whom we share a common goal of making mobility safer and more efficient,” stated Charles Boulanger, LeddarTech’s CEO. “Their strong endorsement is a testament to the maturity of the Leddar technology and the credibility of our product roadmap to support the ongoing development of autonomous driving. This positions LeddarTech as the definitive reference in solid-state LiDAR and validates our Tier-1 partnership business model to rapidly deploy LiDARs on a commercial scale in automotive applications. We believe this announcement represents a major stepping stone towards achieving market-share leadership in the multibillion-dollar automotive LiDAR market by 2020.”


PRNewswire: Innoviz has raised $65m in Series B funding as its LiDAR moves into mass production. Delphi Automotive PLC and Magna International participated in the round, along with additional new investors including 360 Capital Partners, Glory Ventures, Naver and others. All Series A investors, including Zohar Zisapel, Vertex Venture Capital, Magma Venture Partners, Amiti Ventures and Delek Motors, participated in this round. A second closing of this round is expected to be announced soon, introducing additional strategic partners.

"As the autonomous driving market matures, Innoviz has clearly established itself among industry leaders not only as the best-in-class LiDAR solution but also as an integral part of the overall autonomous vehicle stack," said Omer Keilaf, Co-founder and CEO of Innoviz. "Today's financing from strategic partners such as Magna and Delphi demonstrates their support for Innoviz as the industry's leading option for high-performing LiDAR and is further proof that we have moved into the next phase of our growth. Given both Magna's and Delphi's unwavering commitment to high performance and high safety standards, investing in and partnering with Innoviz is a natural choice. With their joint efforts, we will be able to scale more quickly and put autonomous driving technology on the road much faster."

Innoviz technology that leverages the company's proprietary System, MEMS and Detector designs to give autonomous vehicles sensing capabilities even in challenging environments such as bright direct sunlight, varying weather conditions and multi-LiDAR environments.



Oryx Vision announces a $50m Series B funding round. Third Point Ventures and WRV led the round, which was joined by Union Tech Ventures, and existing investors Bessemer Venture Partners, Maniv Mobility and Trucks VC. A mere 15 months after its first funding round, this fundraise brings the total investment in Oryx to $67M.

Oryx will use the new funds to accelerate its development activities and to intensify its commercial engagements with car OEMs, tier-1 supplier and technology players. Having demonstrated the unique capabilities of its technology over the past year, the company expects to ship units for car-mounted testing in the second half of 2018.

“Oryx is building the first solution that will meet all the key requirements of automotive LiDARS – high performance, car durability and low price – without a tradeoff. We are delighted to receive a vote of confidence in our vision from such sophisticated investors, and to have the resources to bring this technology to market quickly and at the highest quality,” says Rani Wellingstein, Oryx co-founder and CEO.

From Oryx Vision presentation:

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Aphesa publishes a blog post "Why You Can't Use A White (Broad Spectrum) Light Source For Your Spectral Measurements." Few quotes:

"The R, G, B or the monochrome curve is not the response curve of the sensor but only some information about it.

First of all, it is usually only measured for an incident beam perpendicular to the sensor, or in other words at a zero chief ray angle. But we know that the spectral response varies in intensity and in shape with the angle of incidence.

Secondly, it is a noisy measurement and therefore the precision depends on the number of samples taken. The measurement is noisy because the amount of light after the monochromator is limited as only a tiny fraction of the source's spectrum reaches the sensor. Therefore the signal level is small and its SNR is therefore low.

The shape of the spectrum also depends on the bandwidth of the measuring instrument. As the response spectrum can exhibit oscillations at several scales (see this other publication about spectral response), only a very small bandwidth, i.e. a narrow monochromator lid, can reproduce the actual shape of the response curve, any other approach will only produce a smoothed curve without any detail.

Finally, the wide band light source will change over time and its spectrum will change over temperature, therefore requiring regular calibration.

The light source that we use is based on a femtosecond laser and a supercontinuum.

The femtosecond laser is a solid laser that provided very high power light pulses with a duration in the range of the femtosecond. Lasers are known to provide coherent, repeatable and high intensity light. Our laser is red and the laser has the size of a small table.

The role of the supercontinuum is to turn the monochromatic laser light into a wide spectrum by a collection of non-linear processes. The supercontinuum is a long microstructured optical fiber. The fiber seems red at its beginning and is white in the end as the spectrum broadens along the fiber."

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Dave Etchells from Imaging Resource publishes a nice overview of Sony image sensor fab. It describes a process of growing the silicon boules by Czochralski method in great details, then gets sketchy on photolitography and other FEOL and BEOL steps, then again goes into much more details on dicing, wire bonding, and packaging. While very simplistic, the article describes quite a complete process flow at Sony Kumamoto fab.

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Espoo-Finland and Cambridge, UK-based startup Emberion is a spin-off of Nokia. The new company works to commercialize graphene-based microbolometric image sensors:

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Fayçal Saffih from University of Waterloo, Canada publishes his video lecture on "Implementations of Intelligence on Smart CMOS Image Sensors" given on Sept. 1, 2017 in York University:

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Huawei unveils Kirin 970 mobile SoC "combining the power of the cloud with the speed and responsiveness of native AI processing."

"Mobile AI = On-Device AI + Cloud AI. HUAWEI is committed to developing smart devices into intelligent devices by building end-to-end capabilities that support coordinated development of chips, devices, and the cloud. The Kirin 970 is the first in a series of new advances that will bring powerful AI features to our devices and take them beyond the competition," says Richard Yu, CEO of Huawei Consumer Business Group.

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Autosens publishes ST presentation on its HDR ISP for automotive cameras from the converence in Detroit in May 2017:

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