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Imaging and Machine Vision Europe publishes a review of LiDAR presentations at Image Sensors Europe conference held in mid-March in London, UK. Few quotes:

"A report from Goldman Sachs Global Investment Research predicts that the market opportunity for lidar in automotive will grow from zero in 2015 to $10 billion by 2025, and $35 billion by 2030.


Oren Rosenzweig, co-founder of Israeli lidar system maker Innoviz Technologies, said at Image Sensors that the cost of lidar is prohibitive, and the performance is not good enough. Rosenzweig, speaking to Imaging and Machine Vision Europe at the show, said that $1,000 per lidar system might be acceptable for certain early adopters of the technology, but that hundreds of dollars per lidar was needed to make it attractive to automotive OEMs. As the volumes increase, however, then costs will go down.

Innoviz’s technology is a solid-state lidar combining a MEMS scanner based on a micro-mirror designed by the company; the signal is processed in a proprietary ASIC. The Innoviz One has a 250-metre detection range, an angular resolution of 0.1 x 0.1 degrees, a frame rate of 25fps, and a depth accuracy of 3cm. The device is based on 905nm laser light; 1,550nm would cost too much for the lasers and detectors, Rosenzweig said.

Solid-state lidar uses primarily 905nm lasers, according to Carl Jackson, founder and CTO of SensL, although he added that 940nm VCSEL arrays are also being developed. Jackson said that SiPMs or a SiPM array can improve sensitivity and ranging compared to avalanche photodiodes (APDs).

SensL’s first product for lidar is a 400 x 100 ToF SPAD array with high dynamic range SPAD pixels, optimised for vertical line scanning. It will be sampling in the second half of 2018. Jackson said that the sensor array can be used to create a lidar solution with 0.1 degree x-y resolution, suitable for greater than 100-metre ranging at 10 per cent reflectivity in full sunlight. Jackson also noted that VGA-quality SPAD arrays could be available next year.

Eye-safety is all about the design of the system, according to Jackson. He said that a laser pulse of 1ns from a 5mm aperture at 30 degrees angle of view in the y direction can reach 26,908W of power and still be eye safe, ‘which is plenty of power to do long-range lidar with 905nm’. He added that a 120 x 30-degree system will need 6,000W of laser power to achieve 200-metre ranging with SiPM technology."

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EETimes publishes an article "Can Huawei Match Apple TrueDepth?" by Junko Yoshida. Few quotes:

Pierre Cambou, activity leader for MEMS and imaging at Yole Développement, predicts that it may take a year or longer for competitors to offer 3D sensing technologies comparable to iPhone X.

...3D sensing will be a tougher challenge for most smartphone vendors — because a 3D camera contains myriad components that need to be aligned. It also requires competent supply chain management. Cambou called the 3D camera “a bundle of sub-devices.”

As for Samsung’s Galaxy S9, some reviewers are already calling its front-facing sensing technology “a disappointment.” ...People were able to fool Samsung's technology on last year's Galaxy S8 by using photos. Apparently, that trick still works with the S9.

Huawei’s triple cameras appear to illustrate the company’s effort to enhance depth-sensing technology. While no confirmation is available, Huawei’s suspected 3D sensing partner is Qualcomm.

SystemPlus' and Yole's cost estimation of iPhone X 3D camera

Reuters shares the same opinion:

Most Android phones will have to wait until 2019 to duplicate the 3D sensing feature behind Apple’s Face ID security, three major parts producers have told Reuters.

According to parts manufacturers Viavi Solutions Inc, Finisar Corp and Ams AG, bottlenecks on key parts will mean mass adoption of 3D sensing will not happen until next year, disappointing earlier expectations.

Tech research house Gartner predicts that by 2021, 40 percent of smartphones will be equipped with 3D cameras, which can also be used for so-called augmented reality.

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PRNewswire: Bozeman, Montana-based Blackmore Sensors and Analytics Inc. has raised $18m in a Series B funding led by BMW i Ventures. Additional investment comes from Toyota AI Ventures, Millennium Technology Value Partners and Next Frontier Capital.

"Blackmore has unique and innovative FMCW lidar technology that delivers a new dimension of data to future vehicles," said BMW i Ventures partner Zach Barasz. In addition to being more cost-effective, Blackmore's FMCW lidar has several advantages over traditional pulsed lidar systems.

"Blackmore's groundbreaking FMCW lidar technology is designed to eliminate interference, improve long-range performance, and support both range and velocity — a triple threat to make autonomous driving safer," said Jim Adler, managing director of Toyota AI Ventures.

According to Randy Reibel, Blackmore's CEO, it is that last capability that differentiates Blackmore's sensor from its competitors. "Having the ability to measure both the speed and the distance to any object gives self-driving systems more information to navigate safely."

Blackmore will use the investment to scale the production of its FMCW lidar for ADAS and self-driving markets. Increased production capacity will allow Blackmore to support the growing sector of autonomous driving teams demanding a superior lidar solution.

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OSA Optics Letters issue dated by April 1st, 2018 publishes Peking University, China, KAIST and KRISS, Korea paper "Time-of-flight detection of femtosecond laser pulses for precise measurement of large microelectronic step height."

"By using time-of-flight detection with fiber-loop optical-microwave phase detectors, precise measurement of large step height is realized. The proposed method shows uncertainties of 15 nm and 6.5 nm at sampling periods of 40 ms and 800 ms, respectively. This method employs only one free-running femtosecond mode-locked laser and requires no scanning of laser repetition rate, making it easier to operate. Precise measurements of 6 μm and 0.5 mm step heights have been demonstrated, which show good functionality of this method for measurement of step heights."

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Techcrunch sources report that Google is acquiring Lytro:

"One source described the deal as an “asset sale” with Lytro going for no more than $40 million. Another source said the price was even lower: $25 million. A third source tells us that not all employees are coming over with the company’s technology: some have already received severance and parted ways with the company, and others have simply left. Assets would presumably also include Lytro’s 59 patents related to light-field and other digital imaging technology.

The sale would be far from a big win for Lytro and its backers. The startup has raised just over $200 million in funding and was valued at around $360 million after its last round in 2017. Its long list of investors include Andreessen Horowitz, Foxconn, GV, Greylock, NEA, Qualcomm Ventures and many more."

Here is NYTimes illustration of Lytro's first product back in 2012:

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Samsung publishes its CIS process features available at 8-inch foundry:


Currently, all 8-inch foundry wafers are processed at Line 6 in Giheung campus, Korea:

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CERN publishes Nobukazu Teranishi's 58 page-large presentation "Recent Progresses of Visible Light Image Sensors" at the Detector Seminar at CERN on February 23, 2018. There is a lot of interesting slides, including spares in the end. Here is just a small part of the content:

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Nikkei publishes a 4-part review of Sony ISSCC 2018 presentation on event-driven sensor:

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ST video presents issues with dirty cover glass in ToF devices, followed by a sort of obvious solution:

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Nocamels, The Times of Israel: Gabby Sarusi from Ben-Gurion University of the Negev "has developed a stamp-like device of which one side reads 1,500-nanometer infrared wavelengths, and converts them to images that are visible to the human eye on the other side of the stamp. This stamp — basically a film that is half a micron in thickness — is composed of nano-metric layers, nano-columns and metal foil, which transform infrared images into visible images.

An infrared sensor costs around $3,000, Sarusi said. A regular vision sensor used by autonomous cars costs $1-$2. So, by adding the nanotech layers, which cost around $5, Sarusi said, one can get an infrared sensor for about $7-$8."






Thanks to DS for the pointer!

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Frost & Sullivan’s Manufacturing Leadership Council prizes Omnivision by Smart Products and Services Leadership Award for Nyxel NIR imaging technology.

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Japanese Journal of Applied Physics publishes Tohoku University paper "Effect of drain current on appearance probability and amplitude of random telegraph noise in low-noise CMOS image sensors" by Shinya Ichino, Takezo Mawaki, Akinobu Teramoto, Rihito Kuroda, Hyeonwoo Park, Shunichi Wakashima, Tetsuya Goto, Tomoyuki Suwa, and Shigetoshi Sugawa. It turns out that lower SF current can reduce RTN, at least for 0.18um process used in the test chip:

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The VL53L1X TOF sensor extends the detection range of ST's FlightSense technology to four meters, bringing high-accuracy, low-power distance measurement, and proximity detection to an even wider variety of applications. The fully integrated VL53L1X measures only 4.9mm x 2.5mm x 1.56mm, allowing use even where space is very limited. It is also pin-compatible with its predecessor, the VL53L0X, allowing easy upgrading of existing products. The compact package contains the laser driver and emitter as well as SPAD array light receiver that gives ST’s FlightSense sensors their ranging speed and reliability. Furthermore, the 940nm emitter, operating in the non-visible spectrum, eliminates distracting light emission and can be hidden behind a protective window without impairing measurement performance.

ST publishes quite a detailed datasheet with the performance data:

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GM has started production of a roof rack for its fourth generation Cruise AV featuring 5 Velodyne LiDARs and, at least, 7 cameras:

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MEMSDrive kindly sent me a presentation on its OIS technology:

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Few assorted pictures from Image Sensors Europe conference being held these days in London, UK.

From Ron (Vision Markets) twitter:


Image Sensors twitter:


From X-Fab presentation:

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Korean newspaper The Investor quotes local media reports that Mantis Vision and camera module maker Namuga are developing 3-D sensing camera for Samsung next-generation Galaxy S smartphones, tentatively called the Galaxy S10. Namuga is also providing 3-D sensing modules for Intel’s RealSense AR cameras.

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TechInsights Samsung Galaxy S9+ cost table estimates cameras cost at $48 out of $379 total. The previous generation S8 camera was estimated at $25.50 or 7.8% of the total BOM.


Update: TechInsights publishes a cost comparison of this year and last yera;s flagship phones. Galaxy S9+ appears to have the largest investment in camera and imaging hardware:

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ICFO food analyzer demo at MWC in Barcelona in February 2018:



UV graphene sensors:

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ETNews reports that Samsung is to convert its 300mm DRAM 13 line in Hwasung to CMOS sensors production. Since last year, the company also working to convert its DRAM 11 line in Hwasung into an image sensor production (named as S4 line). Conversion of S4 line will be done by end of this year. Right after that, Samsung is going to convert its 300mm 13 line. The 13 line can produce about 100,000 DRAM wafers per month. Because image sensor has more manufacturing steps than DRAM, the production capacity is said to be reduced by about 50% after conversion.

“At the end of last year, production capacity of image sensor from 300mm plant based on wafer input was about 45,000 units.” said ETNews source. “Because production capacities of image sensor that will be added from 11 line and 13 line will exceed 70,000 units per month, Samsung Electronics will have production capacity of 120,000 units of image sensor after these conversion processes are over.”

Sony CIS capacity is about 100,000 wafers per month. Even with Sony capacity extension plans are accounted, Samsung should be able to match or exceed Sony production capacity.

While increasing production capacity of 300mm CIS lines for 13MP and larger sensors, Samsung is planning to slowly decrease output of 200mm line located in Giheung.

Samsung capacity expansion demonstrates its market confidence. Samsung believes that its image sensor capabilities approach that of Sony. The number of the company's outside CIS customers is over 10.

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ULIS publishes a promotional video about its capabilities and products:

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One of the largest smartphone makers in China, Vivo, announces its AI-powered Super HDR that follows the same principles as regular multi-frame HDR but merges more frames.

The Super HDR’s DR is said to reach up to 14 EV. With a single press of the shutter, Super HDR captures up to 12 frames, significantly more than former HDR schemes. AI algorithms are used to adapt to different scenarios. The moment the shutter is pressed, the AI will detect the scene to determine the ideal exposure strategy and accordingly select the frames for merging.

Alex Feng, SVP at Vivo says “Vivo continues to push the boundaries and provide the ultimate camera experience for consumers. This goes beyond just adding powerful functions, but to developing innovations that our users can immediately enjoy. Today’s showcase of Super HDR is an example of our continued commitment to mobile photography, to enable our consumers to shoot professional quality photos at the touch of a button. Using intelligent AI, Super HDR can capture more detail under any conditions, without additional demands on the user.”

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EETimes publishes an article on event-driven image sensors such as Prophesee's (former Chronocam) Asynchronous Time-Based Image Sensor (ATIS) chip.

The company CEO Luca Verre "disclosed to us that Prophesee is exploring the possibility that its event-driven approach can apply to other sensors such as lidars and radars. Verre asked: “What if we can steer lidars to capture data focused on only what’s relevant and just the region of interest?” If it can be done, it will not only speed up data acquisition but also reduce the data volume that needs processing.

Phrophesee is currently “evaluating” the idea, said Luca, cautioning that it will take “some months” before the company can reach that conclusion. But he added, “We’re quite confident that we can pull it off.”

Asked about Prophesee’s new idea — to extend the event-driven approach to other sensors — Yole Développement’s analyst Cambou told us, “Merging the advantages of an event-based camera with a lidar (which offers the “Z” information) is extremely interesting.”

Noting that problems with traditional lidars are tied to limited resolution — “relatively less than typical high-end industrial cameras” — and the speed of analysis, Cambou said that the event-driven approach can help improve lidars, “especially for fast and close-by events, such as a pedestrian appearing in front of an autonomous car.”

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TechInsights publishes an article on Galaxy S9+ reverse engineering including its 4 cameras - a dual rear camera, a front camera and an iris recognition sensor:

"We are excited to analyze Samsung's new 3-stack ISOCELL Fast 2L3 and we'll be publishing updates as our labs capture more camera details.

Samsung is not first to market with variable mechanical apertures or 3-layer stacked image sensors, however the integration of both elements in the S-series is a bold move to differentiate from other flagship phones.

The S9 wide-angle camera system, which integrates a 2 Gbit LPDDR4 DRAM, offers similar slo-mo video functionality with 0.2 s of video expanded to 6 s of slo-mo captured at 960 fps. Samsung promotes the memory buffer as beneficial to still photography mode where higher speed readout can reduce motion artifacts and facilitate multi-frame noise reduction."


iFixit reverse engineering report publishes nice pictures showing a changing aperture on the wide-angle rear camera:

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Phil Garrou's IFTLE 374 reviews 3DInCites Award winners. Two of them are related to image sensors:

Device of the Year: OS05A20 Image Sensor with Nyxel Technology, OmniVision:

"OmniVision’s OS05A20 Image Sensor was nominated for being the first of its image sensors to be built with Nyxel ™ Technology. This approach to near-infrared (NIR) imaging combines thick-silicon pixel architectures with careful management of wafer surface texture to improve quantum efficiency (QE), and extended deep trench isolation to help retain modulation transfer function without affecting the sensor’s dark current. As a result, this image sensor sees better and farther under low- and no-light conditions than previous generations."

Engineer of the Year: Gill Fountain, Xperi:

"Known as Xperi’s guru on Ziptronix’ technologies, Gill was nominated for his most recent contribution, expanding the chemical mechanical polishing process window for Cu damascene from relatively fine features. His team developed a process that delivers uniform, smooth Cu/Ta/Oxide surfaces with a controlled Cu recess with very small variance across wafer sizes. He has been an integral part of Xperi’s technical team and his work allows the electronics industry to apply direct bond interconnect (DBI) for high-volume wafer-to-wafer applications."

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IEEE publishes an interview with Steven J. Sasson who invented the first digital camera in 1975 while working at Eastman Kodak, in Rochester, N.Y. A notable Q&A:

Q: What tech advance in recent years has surprised you the most?

A: Cameras are everywhere! I would have never anticipated how ubiquitous the imaging of everything would become. Photos have become the universal form of casual conversation. And cameras are present in almost every type of environment, including in our own homes. I grossly underestimated how quickly it would take for us to get here.

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Hamamatsu publishes a beer identification article showing it as an application for its micro-spectrometers:

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BusinessWire: Forza Silicon CTO, Daniel Van Blerkom, is to present a paper titled “Accelerated Image Sensor Production Using Machine Learning and Data Analytics” at Image Sensors Europe 2018 in London on March 15, 2018.

The machine learning has been applied to sensor data sets to identify and measure critical yield limiting defects. “Image sensors offer the unique opportunity to image the yield limiting defect mechanisms in silicon,” said Daniel Van Blerkom. “By applying machine learning to image sensor test procedures we’re able to quickly and easily classify sensor defects, identify root-cause and feedback the results to improve the process, manufacturing flow and sensor design for our clients.”

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