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Barron's quotes financial analyst Jun Zhang of Rosenblat saying:

"As we look across the landscape it appears to us that HIMX continues to struggle to find OEMs to incorporate its solution as our latest industry research suggests OPPO is working with Orbbec, Xiaomi with O-film (002456-SZ:NR) & Mantis Vision for its Mi7 Plus and Huawei on an internal solution. We also be- lieve other tier-2 and 3 OEMs are targeting a 2019 launch for their phones. On the conference call, management commented that its 3D sensing solution will be ready for mass production in Q2 but did not announce any design wins. Based on the long lead times for 3D sensing modules, Himax should have needed to have already secured a design-win if to be part of any solution."

SeekingAlpha earnings call transcript has the company's CEO Jordan Wu predicting: "3D sensing will be our biggest long term growth engine and, for 2018, a major contributor to both revenue and profits, consequently creating a more favorable product mix for Himax starting the second half of 2018."

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BusinessWire: Teledyne DALSA completed the qual of a its Wafer-Level-Packaged Vanadium Oxide (VOx) Microbolometer process for LWIR imaging.

Teledyne DALSA’s manufacturing process, located in its MEMS foundry in Bromont, Quebec, bonds two 200 mm wafers precisely and under high vacuum, forming an extremely compact 3D stack. This technology eliminates the need for conventional chip packaging - which can account for 75% or more of the overall device cost.

“This is an important milestone in our journey to bring a credible price/performance VOx solution to market,” said Robert Mehrabian, Chairman, President and CEO of Teledyne. “With the qualification process complete we will now begin ramping up production lines for a 17-micron pixel 320×240 (QVGA) device, closely followed by a 17-micron 640×480 (VGA), with longer-term plans to introduce a highly compact 12-micron detector family.”

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Albert Theuwissen publishes a review of ISSCC papers, starting from Sony BSI-GS CMOS imager with pixel-parallel 14b ADC: "One can make a global shutter in a CMOS sensor in the charge domain, in the voltage domain, but also in the digital domain. The latter requires an ADC per pixel (also known as DPS : digital pixel sensor). And this paper describes such a solution : a stacked image sensor with per pixel a single ADC."

Panasonic organic sensor: "The paper claims that the reset noise is lowered by a factor of 10, while the saturation level is increased by a factor of 10 (but the high saturation mode cannot be combined with the low noise level)."

Samsung 24MP CIS with 0.9um pixel: "All techniques mentioned are not new, but their combination for a 0.9 um is new."

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Omnivision publishes HDR marketing video:

Update: Omnivision removed the video and sent me the following update:

"The video was made public by mistake. Once we finalize the video we will re-publish and share with OmniVision’s customers and media contacts."

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Sony presents 1.46MP stacked BSI CMOS sensor with Global Shutter and newly developed low power pixel-parallel ADC to convert the analog signal from all pixels, simultaneously exposed, to a digital signal in parallel.

The inclusion of nearly 1,000 times as many ADCs compared to the traditional column-parallel ADC architecture means an increased demand for current. Sony addressed this issue by developing a compact 14-bit A/D converter which is said to boast the industry's best performance in low-current operation. The FoM of the new ADC is 0.24e-・nJ/step. (power consumption x noise) / {no. of pixels x frame speed x 2^(ADC resolution)}.

The connection between each pixel on the top chip uses Cu-Cu connection, that Sony put into mass production as a world-first in January 2016.

Main Features:

• Low-current, compact pixel-parallel A/D converter
  In order to curtail power consumption, the new converter uses comparators that operate with subthreshold currents, resulting in the low current, compact 14-bit ADC. This overcomes the issue of the increased demand for current due to the inclusion of nearly 1,000 times as many ADCs in comparison with the traditional column ADC.
• Cu-Cu (copper-copper) connection
  To achieve the parallel A/D conversion for all pixels, Sony has developed a technology which makes it possible to include approximately three million Cu-Cu (copper-copper) connections in one sensor. The Cu-Cu connection provides electrical continuity between the pixel and logic substrate, while securing space for implementing as many as 1.46 million A/D converters, the same number as the effective megapixels, as well as the digital memory.
• High-speed data transfer construction
  Sony has developed a new readout circuit to support the massively parallel digital signal transfer required in the A/D conversion process using 1.46 million A/D converters, making it possible to read and write all the pixel signals at high speed.

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Hamamatsu publishes two more videos - "LiDAR, Radar, and Cameras: Measuring distance with light in the automotive industry" and "SiPM: Operation, performance, and possible applications," both by Slawomir Piatek, senior lecturer of physics at New Jersey Institute of Technology.

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Sony explains its Safety Cocoon concept:

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Hamamtsu publishes two hour-long educational webcasts "Silicon photomultipliers: theory & practice" and "Low light detection: PMT vs. SiPM."

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Jaroslav Hynecek has been elevated to IEEE fellow for contributions to solid-state image sensors.

Thanks to NT for the info!

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The Verge reports that China police is testing facial recognition glasses at train stations in the “emerging megacity” of Zhengzhou, where they’ll be used to scan travelers during the upcoming Lunar New Year migration.

The glasses are developed by Beijing-based LLVision Technology Co. The company says they’re able to recognize individuals from a pre-loaded database of 10,000 suspects in just 100ms, but cautions that accuracy levels in real-life usage may vary due “environmental noise.”

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SPIE publishes FBK paper and presentation video of "SUPERTWIN: towards 100kpixel CMOS quantum image sensors for quantum optics applications" by Leonardo Gasparini; Bänz Bessire; Manuel Unternährer; André Stefanov; Dmitri Boiko; Matteo Perenzoni; David Stoppa.

"Quantum imaging uses entangled photons to overcome the limits of a classical-light apparatus in terms of image quality, beating the standard shot-noise limit, and exceeding the Abbe diffraction limit for resolution. In today experiments, the spatial properties of entangled photons are recorded by means of complex and slow setups that include either the motorized scanning of single-pixel single-photon detectors, such as Photo-Multiplier Tubes (PMT) or Silicon Photo- Multipliers (SiPM), or the use of low frame rate intensified CCD cameras. CMOS arrays of Single Photon Avalanche Diodes (SPAD) represent a relatively recent technology that may lead to simpler setups and faster acquisition. They are spatially- and time-resolved single-photon detectors, i.e. they can provide the position within the array and the time of arrival of every detected photon with less than 100 ps resolution. SUPERTWIN is a European H2020 project aiming at developing the technological building blocks (emitter, detector and system) for a new, all solid-state quantum microscope system exploiting entangled photons to overcome the Rayleigh limit, targeting a resolution of 40nm. This work provides the measurement results of the 2nd order cross-correlation function relative to a flux of entangled photon pairs acquired with a fully digital 8×16 pixel SPAD array in CMOS technology. The limitations for application in quantum optics of the employed architecture and of other solutions in the literature will be analyzed, with emphasis on crosstalk. Then, the specifications for a dedicated detector will be given, paving the way for future implementations of 100kpixel Quantum Image Sensors."

The trend in CMOS SPAD-array design goes towards:

(i) the miniaturization of the pixel (below 10µm) to increase the output image resolution;

(ii) SPAD optimization to improve the photon detection efficiency (PDE) while reducing DCR, after-pulsing and crosstalk;

(iii) 3D stacking of chips, with a top tier optimized for sensing that includes the array of SPADs and a bottom tier optimized for processing (i.e., counting, timestamping and buffering);

(iv) smart mechanisms for timestamping photons, such TDC sharing and time-gated counting in the analog domain;

(v) the on-chip implementation of pre-processing stages, such as timestamp histogramming, to reduce the sensor output data size and increase the frame rate, thus enabling synchronization with fast sources of photons (from 100kHz up to tens of MHz).

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Open-source Elphel camera project that uses quad camera for 3D vision, now adds Convolutional Neural Network with intention to reach the depth range of few hundreds to thousands meters with cameras spaced apart by just 150mm:

"We plan to fuse the methods of high resolution images calibration and processing, already emulated functionality of the Tile Processor (TP), RTL code developed for its implementation and the Convolutional Neural Network (CNN). Compared to the CNN alone this approach promises over a hundred times reduction in the number of input features without sacrificing universality of the end-to-end processing. The TP part of the system is responsible for the high resolution aspects of the image acquisition (such as optical aberrations correction and image rectification), preserves deep sub-pixel super-resolution using efficient implementation of the 2-D linear transforms. Tile processor is free of any training, only a few hyperparameters define its operation, all the application-specific processing and “decision making” is delegated to the CNN."

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SeekingAlpha publishes SMIC earnings call transcript with update on its CIS business:

"We have already pinpointed a number of key platforms to address and today I'll highlight two of them. Our NOR flash platform and CMOS image sensor platform. These two have revenue to SMIC grow almost 70% in last year, compared with the year before. We continue to build on our platform strategy and seek to expand our customers' business. We are working hard to implement this market adjustment strategy within the company."

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Fuse publishes few more slides from Sony presentation on 3-layer chip stacking flow at IEDM 2017.

"The final product is an impressive 19.3M pixels of 1.22 x 1.22 μm each and a 1 Gbit DRAM. Sony used TSVs that have a minimum diameter of 2.5 μm and a pitch of 6.3 μm with a line of 2 μm and space of 0.64 μm. In total they have over 35,000 TSVs – about 15,000 connecting the pixel substrate and the DRAM substrate and about 20,000 more connecting the DRAM substrate to the logic substrate.

The chip achieved 120 fps for all 19.3M pixels and can produce 960 fps FHD (1,920 x 1,080) super slow motion video."

3-layer stacking process flow

TEM cross-section

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Velodyne publishes a video on LiDAR advantages in automotive applications:




SeekingAlpha publishes Tesla Q4 2017 earnings call transcript with CEO Elon Musk saying:

Q: "Elon, on your autonomous vehicle strategy, why do you believe that your current hardware set of only camera plus radar is going to be able to get you to fully-validated autonomous vehicle system? Most of your competitors noted that they need redundancy from lidar hardware to given the robustness of the 3D point cloud and the data that's generated. What are they missing in their software stack and their algorithms that Tesla is able to obtain from just the camera and plus radar?

Further, what would be your response if the regulatory bodies required that level of redundancy is really needed from an incremental lidar hardware?"

Elon Musk: "Well, first of all, I should say there's actually three sensor systems. There are cameras, redundant forward cameras, there's the forward radar, and there are the ultrasonics for near field. So, the third is also – the third set is also important for near-field stuff, just as it is for human.

But I think it's pretty obvious that the road system is geared towards passive optical. We have to solve passive optical image recognition, extremely well in order to be able to drive in any given environment and the changing environment. We must solve passive optical image recognition. We must solve it extremely well.

At the point at which you have solved it extremely well, what is the point in having active optical, meaning lidar, which does not – which cannot read signs; it's just giving you – in my view, it is a crutch that will drive companies to a local maximum that they will find very difficult to get out of.

If you take the hard path of a sophisticated neural net that's capable of advanced image recognition, then I think you achieve the goal maximum. And you combine that with increasingly sophisticated radar and if you're going to pick active photon generator, doing so in 400 nanometer to 700 nanometer wavelength is pretty silly, since you're getting that passively.

You would want to do active photon generation in the radar frequencies of approximately around 4 millimeters because that is occlusion penetrating. And you can essentially see through snow, rain, dust, fog, anything. So, it's just I find it quite puzzling that companies would choose to do an active photon system in the wrong wavelength. They're going to have a whole bunch of expensive equipment, most of which makes the car expensive, ugly and unnecessary. And I think they will find themselves at a competitive disadvantage.

Now perhaps I am wrong. In which case, I'll look like a fool. But I am quite certain that I am not."

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Imec lens-free microscopy leverages super small pixels in modern sensors and currently uses 1.1um pixel pitch. Imec uses a specialty laser based illumination coupled with holographic software for the image reconstruction with better than 1um resolution images with fields of view as large as 20 - 40 mm2 in a very low cost and compact form factor, unmatched by any microscope technology so far.

If any CIS manufacturer would be able to bring to market in near future much smaller pixels (e.g. 0.6um) with large enough resolution, IMEC expects it would enable a major breakthrough where characterization of even smaller objects below 500nm resolution like proteins, bacteria’s and fine particles matter pollution would be finally possible.

Imec demos its lens-free platform in Vimeo video:

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Image Sensors Europe to be held in mid-March 2018 in London, UK publishes a number of interviews ahead of the conference.

Ian Riches, Global Automotive Practice from Strategy Analytics:

Q: What do you see as the most significant changes coming up in vision systems development and their applications within automotive in the next 12-24 months?

A:
a) Much more use of machine vision in the currently largely “dumb” applications of park assist and surround view.
b) Camera resolutions markedly increasing
c) A lot more in-cabin sensing

Albert Theuwissen, Founder of Harvest Imaging:

There are several reasons why I think (= am convinced) that monolithic CMOS imagers are superior to hybrid imagers :

• The hybrid imagers are always based on 3T structures, while monolithic can make use of the 4T imagers. This results in a lower noise for the latter,
• The dark current, dark current non-uniformities and isolated hot-pixel count is always better for monolithic silicon,
• Monolithic silicon has improved quite a lot w.r.t. their response to near-IR light, such that they show even better (QE) performance in the near-IR than most of the hybrid imagers,
• Monolithic silicon has a better signal-to-noise ratio than the hybrid imagers.

These statements are valid within the wavelength range of monolithic silicon (visible spectrum up to 1.1 um). Outside this wavelength range, the story can be completely different.

Q: What would you say are the 3 biggest game changers that will soon hit the image sensors industry and how can we prepare for it?

A:

1. The increase of stacked imager technology - more companies are following this trend outside of just image-sensor companies, for example companies that (will) have signal-processing chips available that can be stacked to imagers. On the other hand, the stacking technology is quickly moving to the stacking on pixel level. This will result in ultra-fast devices with a huge amount of parallel processing capabilities on the (stacked) chip.
2. The use of near-IR information will not only add more features to the cameras, but also introduce new applications, e.g. face recognition in mobile phones, measurement of distances, etc.
3. Imagers are no longer mainly used for making beautiful images, but more and more applications are being created to use image sensors for totally other functions. Examples are the time-of-flight applications, the auto focus pixels, use in autonomous driving cars, etc.

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PrZen: Gigajot has been awarded a National Science Foundation (NSF) Small Business Innovation Research (SBIR) grant for $225,000 to conduct R&D on a high-speed, high-resolution and high-sensitivity camera. This Quanta Image Sensor (QIS) camera will be the first megapixel CMOS camera in the market with photon-counting capability and room temperature operation. This prototype product can be beneficial in scientific and medical imaging, life science, astronomical imaging, and other applications.

"Scientists and medical doctors are not satisfied with the cameras they use in the laboratories and hospitals," said Saleh Masoodian, CEO, Gigajot. "By implementing the Gigajot's QIS devices into the scientific and medical cameras, scientists and researchers will be able to conduct more accurate measurements and researches with the innovative imaging technology."

"The novel technology is based on the mainstream commercial CMOS fabrication processes to realize high-yield and low-cost production. Besides the scientific imaging products, this technology will ultimately improve the performance of consumer imaging devices," said Jiaju Ma, CTO, Gigajot.

Once a small business is awarded a Phase I SBIR/STTR grant (up to $225,000), it becomes eligible to apply for a Phase II grant (up to $750,000). Small businesses with Phase II grants are eligible to receive up to $500,000 in additional matching funds with qualifying third-party investment or sales.


Yahoo: A 12-year old 3D structured light camera maker Mantis Vision is about to get $36m investment from China-based Luenmei Quantum Ltd.

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DxOMark publishes an article "Disruptive technologies in mobile imaging: Taking smartphone cameras to the next level" reviewing smartphone camera technologies and their impact on image quality measurements from 2012 to today. One can clearly see a saturation in improvements over the last couple of years:

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ON Semi updates on its image sensor business in the quarterly report:

"Our momentum in ADAS market continues to accelerate with strong demand for our 1 Megapixel and 2 Megapixel image sensors. We recently launched an 8 Megapixel image sensor for ADAS, and with this launch we are the only provider of complete line of 1, 2 and 8 megapixel image sensors on a single platform for next generation ADAS and autonomous driving systems. A complete line of image sensors on a single platform reduces qualification time, lowers cost for our customers, and enables our customers to seamlessly port their algorithms across our product line. We believe that our complete line of image sensors on a single platform provides us with a significant competitive advantage and further extends our technology lead over our competitors.

A superior product portfolio complemented by a large installed base, deep and long term relationships with leading ADAS and autonomous driving processor companies, our leading edge technology including self-correcting sensors with on-board cyber security and functional safety, and a strong application support has helped us strengthen our position within the ADAS and autonomous driving ecosystem. Based on inputs from our ecosystem partners, we believe that we are now the lead imaging partner for all ADAS platform providers. We further solidified our presence in the ADAS and autonomous driving eco-system with our recently announced partnership with Baidu. We were recently selected by Baidu as the sole supplier of image sensors for its Apollo autonomous driving platform.

We broke a record of shipments with our Python line of image sensors in 2017 and we are in the process of launching a new platform of products to address a wider swath of the industrial market. The new machine vision product platform will bring unprecedented levels of image quality, speed, pixel technology and price to the fast growing machine vision industry. We continue to develop synergies with our expertise in the automotive imaging market to accelerate our growth in the machine vision market as both these markets are driven by artificial intelligence and face similar challenges, such as low light conditions, dynamic range and harsh operating environment."

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Hamamatsu publishes a number of videos showing its image sensor improvements:

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SystemPlus publishes reverse enginering reports on iPhone X ToF Proximity Sensor & Flood Illuminator by ST and IR Dot Projector with folded optics.

"Located in the front above the main speaker, the proximity sensor/flood illuminator is packaged using optical LGA. The device is unique to Apple. In the past, STMicroelectronics released a custom proximity sensor two times smaller (2.80 x 2.40 mm) than the devices in its portfolio, based on FlightSense technology. This new version comes with a flood illuminator used in the True Depth system. The flood illuminator is an NIR VCSEL with a wide beam that illuminates the face and allows for nighttime user recognition with the NIR camera. The packaging is specially designed for the flood illuminator’s heat management."

ST Proximity Sensor

The structured light dot projector features 4 innovations:

• Its packaging exploits a novel thermal management approach with ceramic substrate inserted
• Its NIR VCSEL diode has a dedicated light emission profile driven by a Broadcom integrated circuit
• A folded optical path using wafer level optic
• Its active diffractive optical element (DOE)

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Samsung introduces a dual camera solution with ISOCELL Dual image sensors and proprietary software for two popular features – refocusing (bokeh) and low-light shooting (LLS). While such dual camera features had generally been exclusive to premium smartphones, Samsung’s ISOCELL Dual sensors and its library of proprietary software algorithms enable these features in lower price mobile devices.

Samsung is coupling its refocusing algorithm with a 13MP and 5MP set of CMOS sensors, and its LLS algorithm with a set of two 8MP sensors, to simplify implementation by OEMs.

“Dual cameras are delivering new and exciting photo-taking experiences on mobile devices,” said Ben K. Hur, VP of System LSI marketing at Samsung Electronics. “Samsung’s total solution for ISOCELL Dual will make our customers’ product development process easier, allowing them to bring the most optimized dual camera features to a wider range of consumers.”

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CEA-Leti presents its curved sensor technology:

"Curved sensor technology is a disruptive approach for imaging applications such as photography, videography, computer vision, surveillance and many other applications," says Leti researcher Bertrand Chambion. "In recent years, we have seen very strong interest in curved electronics, particularly for opto-electronics systems whose performance improves, while size, complexity and cost are reduced."

Leti demo is based on a 1/1.8-inch 1.3MP CMOS sensor. The standard sensor structure consists of a 7.74 x 8.12 mm silicon die glued on a ceramic package. Electrical connections are wire bonded from the die to the package surface and, then, to the interconnection board. A glass cover is placed on top for mechanical protection.

Leti uses a grinding process to get the sensor below 100µm thick, which makes it mechanically flexible. It is then glued onto a curved substrate, which determines its final shape. A wire bonding process developed for electrical connections is used to prevent damages on the thinned dies. The radius of curvature is R=65mm.

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Hamamatsu publishes a webinar "Silicon Photomultiplier: Operation, Performance & Possible Applications" by Slawomir Piatek. Few slides out of deck of 70:

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