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SeekingAlpha publishes ST Q2 earnings call with updates on the company's imaging business:

"Net revenues were $2.09 billion, down 6.5% on a sequential basis. As expected, this was due to a decline in Automotive, Analog and Imaging products, partially offset by growth in Microcontrollers, Digital and Power Discrete.

Net revenues decreased 4% year-over-year with lower sales in Imaging, Automotive and MEMS, partially offset by higher sales in microcontrollers, digital, analog and power discrete.

AMS (Division] revenues decreased 10.1% with MEMS and Imaging lower while Analog sales were higher.

During the quarter, we also won sockets for our Global Shutter Automotive Imaging Solution for driver monitoring systems from two major OEMs. And this is an important step in our diversification strategy related to optical sensing solutions."

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PRNewswire: SmartSens announces the SC500AI widescreen smart image sensor. The majority of 5MP security camera sensors offer a 4:3 aspect ratio that is not optimized for the widescreen format of modern LCD displays. The SmartSens SC500AI addresses this shortcoming with 1620P 16:9 5MP video output in the same form factor.

In a side-by-side comparison with SmartSens' previous generation sensor, the new SC500AI reduces the dark current from 389 e- at 80˚C to 210 e-. The total RN, or Read Noise, is reduced from 0.75e- to 0.63e-. And the sensitivity level also shows a noticeable improvement, growing from 2800 mV/lux-sec to 3680 mV/lux-sec.

These improvements are made possible by SmartSens' unique SFCPixel technology, which takes advantage of the close proximity between the source follower and the photodiodes to increase the sensitivity level, producing high-quality night-vision images. SmartSens' proprietary PixGain technology additionally enables the sensor to achieve excellent HDR performance even under glaring sunlight.

Existing customers of SmartSens' previous-generation products of P2P will see a hassle-free system upgrade to the SC500AI, which is compatible with 1/3-inch 5MP lenses in a wide array for professional security products.

"SmartSens continues to build on its AI series of sensors, offering our customers a range of solutions utilizing the latest photosensor technologies to address the most challenging lighting conditions," said Chris Yu, Chief Marketing Officer of SmartSens. "We continue to strengthen our portfolio to match new applications and our customers' quickly-evolving needs."

The SC500AI Image Sensor is available for testing immediately.

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MDPI paper "Development of Reliable, High Performance WLCSP for BSI CMOS Image Sensor for Automotive Application" by Tianshen Zhou, Shuying Ma, Daquan Yu, Ming Li, and Tao Hang from Shanghai Jiao Tong University, Xiamen University, and Huatian Technology (Kunshan) Electronics belongs to a Special Issue "Smart Image Sensors."

"To meet the urgent market demand for small package size and high reliability performance for automotive CMOS image sensor (CIS) application, wafer level chip scale packaging (WLCSP) technology using through silicon vias (TSV) needs to be developed to replace current chip on board (COB) packages. In this paper, a WLCSP with the size of 5.82 mm × 5.22 mm and thickness of 850 μm was developed for the backside illumination (BSI) CIS chip using a 65 nm node with a size of 5.8 mm × 5.2 mm. The packaged product has 1392 × 976 pixels and a resolution of up to 60 frames per second with more than 120 dB dynamic range. The structure of the 3D package was designed and the key fabrication processes on a 12” inch wafer were investigated. More than 98% yield and excellent optical performance of the CIS package was achieved after process optimization. The final packages were qualified by AEC-Q100 Grade 2."

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GlobeNewswire: ST reports that Aura Aware is using ST’s FlightSense technology in a smart distance-awareness portable device suitable for use at retail counters and check-in desks. The easy-to-setup device displays a green OK signal that changes to red if a person crosses a safe minimum-distance threshold.



AnandTech reports that after more than four years of being acquired by Sony, Altair Semiconductor is renaming itself as Sony Semiconductor Israel. The AI inference processor that’s been integrated into the new IMX500/501 sensors, was developed by Altair/Sony Semiconductor Israel.

"We have been honored to be part of Sony for the past four years, playing a key role in the company’s core business,” says Sony Semiconductor Israel CEO Nohik Semel, “To better reflect our long-term commitment to our partners and customers, as well as the quality of our offering, we have decided to change Altair’s company name to Sony.”


ON Semi publishes a promotional video about robotic vision applications:



Digitimes reports that ASE starts mass production of LiDAR modules in 2H2020: Taiwan's backend house ASE Technology is expected to start volume production of LiDAR modules in the second half of 2020 as it has indirectly entered supply chains of first-tier automakers through its international clients. ASE Technology is said to aggressively incorporate AI technology to support smart production of ToF LiDARs.

Talking about LiDARs, Forbes contributor Sabbir Rangwala publishes a comparison table of possible spots for LiDAR in a car:


Edinburgh, UK-based Photon Force, a provider of time-resolved SPAD cameras, has received a Business Start-Up Award from the Institute of Physics (IOP).

"Founded in 2015 as a spin-out from Robert Henderson’s renowned CMOS Sensors and Systems Group at the University of Edinburgh, Photon Force has won the IOP accolade for the development of its ground-breaking sensors that enable ultrafast, single photon sensitive imaging. Photon Force sensors are used worldwide and facilitate progress in applications including quantum physics, communications and biomedical imaging/neuroscience."

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INL - Institut des Nanotechnologies de Lyon, France, publishes a PhD Thesis "Integration of Single Photon Avalanche Diodes in Fully Depleted Silicon-on-Insulator Technology" by Tulio Chaves de Albuquerque. It starts with a nice introduction into generic SPAD technology and then goes into its integration into FDSOI process.

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Intechopen publishes a book chapter "AMOLED Displays with In-Pixel Photodetector" by By Nikolaos Papadopoulos, Pawel Malinowski, Lynn Verschueren, Tung Huei Ke, Auke Jisk Kronemeijer, Jan Genoe, Wim Dehaene, and Kris Myny from Imec.

"The focus of this chapter is to consider additional functionalities beyond the regular display function of an active matrix organic light-emitting diode (AMOLED) display. We will discuss how to improve the resolution of the array with OLED lithography pushing to AR/VR standards. Also, the chapter will give an insight into pixel design and layout with a strong focus on high resolution, enabling open areas in pixels for additional functionalities. An example of such additional functionalities would be to include a photodetector in pixel, requiring the need to include in-panel TFT readout at the peripherals of the full-display sensor array for applications such as finger and palmprint sensing."


Meanwhile, Vkansee works with China-based Tianma to productize its on-dusplay optical fingerprint sensor:

"Vkansee’s proprietary Matrix Pinhole Image Sensing (MAPIS) – is integrated into the mobile phone OLED display panel, effectively turning the entire display into a high-resolution fingerprint lens allowing simple installation of the image sensor anywhere or everywhere under the OLED display screen. Unlike other solutions that implement FOD and yield poor quality fingerprint images, the MAPIS OLED solution captures high-quality images, because the in-panel optical design avoids the influence of obstructing TFT driver circuits."

“We are focused on bringing our novel MAPIS optical fingerprinting technology to users across the globe to improve security and convenience, and hope to make MAPIS optics as a standard design of OLED,” stated Jason Chaikin, President of VKANSEE. “In partnership with Tianma, we’re confident this will happen in the near future. We believe integrating the MAPIS optics into the OLED screen will greatly change the fingerprint sensor industry similar to the history of integrating touch sensing into the OLED screen.”

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Gene Peter Weckler died of complications from Alzheimer’s on December 3, 2019. He was 87 years old.

Among his significant contributions to image sensor technology, in 1967 Gene published a seminal paper entitled: “Operation of pn junction photodetectors in a photon flux integrating mode,” which was published in the IEEE J. Solid-State Circuits. Nearly every image sensor built since then has operated in this mode. Gene also published several early papers on what we now call passive pixel image sensors during his time at Fairchild.

In 1971 he co-founded RETICON to further commercialize the technology. RETICON was acquired by EG&G in 1977. Gene stayed with EG&G for twenty years serving in many management roles including Director of Technology for the Opto Divisions. In 1997 Gene co-founded Rad-icon to commercialize the use of CMOS-based solid-state image sensors for use in x-ray imaging. Rad-icon was acquired by DALSA in 2008. Gene retired from full time work in 2009 but continued as a member of the Advisory Board for the College of Engineering at Utah State University.

In 2013, Gene Weckler received International Image Sensor Society (IISS) Exceptional Lifetime Achievement Award.

An oral history recording can be found here: http://www.semiconductormuseum.com/Transistors/ShockleyTransistor/OralHistories/Weckler/Weckler_Index.htm

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Purdue University publishes its paper presented at 16th European Conference on Computer Vision (ECCV) 2020 "Dynamic Low-light Imaging with Quanta Image Sensors" by Yiheng Chi, Abhiram Gnanasambandam, Vladlen Koltun, and Stanley H. Chan.

"Imaging in low light is difficult because the number of photons arriving at the sensor is low. Imaging dynamic scenes in low-light environments is even more difficult because as the scene moves, pixels in adjacent frames need to be aligned before they can be denoised. Conventional CMOS image sensors (CIS) are at a particular disadvantage in dynamic low-light settings because the exposure cannot be too short lest the read noise overwhelms the signal. We propose a solution using Quanta Image Sensors (QIS) and present a new image reconstruction algorithm. QIS are single-photon image sensors with photon counting capabilities. Studies over the past decade have confirmed the effectiveness of QIS for low-light imaging but reconstruction algorithms for dynamic scenes in low light remain an open problem. We fill the gap by proposing a student-teacher training protocol that transfers knowledge from a motion teacher and a denoising teacher to a student network. We show that dynamic scenes can be reconstructed from a burst of frames at a photon level of 1 photon per pixel per frame. Experimental results confirm the advantages of the proposed method compared to existing methods."

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CnTechPost: Chinese Alipay insurance platform has announced the opening of pet nose-print recognition technology and has joined forces with insurers to apply this technology to dogs and cats insurance for the first time.

According to Alipay, the success rate of pet nose-print recognition technology exceeds 99% and is expected to be applied to urban pet management and lost pet scenarios in the future.

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Microsoft Research works on embedding a camera under a display for videoconferencing:

"From the earliest days of videoconferencing it was recognized that the separation of the camera and the display meant the system could not convey gaze awareness accurately. Videoconferencing systems remain unable to recreate eye contact—a key element of effective communication.

Locating the camera above the display results in a vantage point that’s different from a face-to-face conversation, especially with large displays, which can create a sense of looking down on the person speaking.

Worse, the distance between the camera and the display mean that the participants will not experience a sense of eye contact. If I look directly into your eyes on the screen, you will see me apparently gazing below your face. Conversely, if I look directly into the camera to give you a sense that I am looking into your eyes, I’m no longer in fact able to see your eyes, and I may miss subtle non-verbal feedback cues."


"With transparent OLED displays (T-OLED), we can position a camera behind the screen, potentially solving the perspective problem. But because the screen is not fully transparent, looking through it degrades image quality by introducing diffraction and noise.

To compensate for the image degradation inherent in photographing through a T-OLED screen, we used a U-Net neural-network structure that both improves the signal-to-noise ratio and de-blurs the image.

We were able to achieve a recovered image that is virtually indistinguishable from an image that was photographed directly."


Via MSPowerUser

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PRNewswire: Unispectral announces what it calls the industry’s first mass market ColorIR Tunable NIR filter and spectral IR camera. Unispectral’s tunable filter turns low cost IR cameras into 700-950nm spectral cameras. It is best suited for facial recognition, consumer portable devices, IOT, robotics and mass market cameras. ColorIR products enable advanced machine vision, material sensing and computational photography.

The core product consists of a tunable MEMS filter assembled on a camera module. RaspberryPi is used to capture parameters and interface by USB or WiFi toPC or Mobile device. SDK is included to develop additional applications.

“Our excellent team is proud to roll out this tunable filter which connects seeing with sensing. It makes spectral cameras accessible for mass-market platforms. The market strives to find an effective solution for adding spectral information to cameras and we believe our technology offers the best blend of performance, and cost,” said Ariel Raz, CEO of Unispectral.

The ColorIR camera captures multiple frames in different NIR wavelengths, filtered by a miniature Fabry–Pérot optical cavity MEMS element. This unique solution breaks the price for legacy spectral cameras, thereby enabling new markets and use cases.

Use Cases of ColorIR:

• Security Market: Facial Authentication, Access Control, Payment Terminals, Fake Bills detection,
• Smartphone Camera: image enhancement, , , low light and shadow picture corrections,
• Medical Market: Remote health inspection
• Agriculture: Fruit inspection, Pesticide detection
• Industrial: Production line Inspection,
• Vehicle: DMS

The ColorIR tunable Mems EVK is available for pre-order. Shipping is planned for end of July.

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ResearchGate: Glasgow University, UK, paper "How many photons does it take to form an image?" by Steven D. Johnson, Paul-Antoine Moreau, Thomas Gregory, and Miles J. Padgetta tries to answer on a somewhat philosophical question:

"If a picture tells a thousand words, then we might ask ourselves how many photons does it take to form a picture? In terms of the transmission of the picture information, then the multiple degrees of freedom (e.g., wavelength, polarization, and spatial mode) of the photon mean that high amounts of information can be encoded such that the many pixel values of an image can, in principle, be communicated by a single photon. However, the number of photons required to transmit the image information is not necessarily, at least technically, the same as the number of photons required to image an object. Therefore, another equally important question is how many photons does it take to measure an unknown image?

For intensity images, it seems that one detected photon per image pixel is a realistic guide, but this may be reduced by making further assumptions on the sparsity of an image in a chosen basis, such as spatial frequency. In this last respect, the advent of machine learning, knowledge-based reconstruction, and similar techniques alleviates the need for a user to explicitly define the sparse basis, but rather the prior is determined from a library of previously recorded images of a similar type. This machine learnt prior can then potentially be designed into the optimum measurement strategy. It seems likely therefore that future imaging systems will combine state-of-the-art single photon detectors with knowledge-based processing both in the design of the system itself and in the processing of the collected data to yield images or decisions based on these data on the basis of extremely low numbers of photons, potentially well below one photon per image pixel."


Once we are at single-photon imaging, the International SPAD Sensor Workshop (ISSW 2020) held as an on-line event in June published a nice SPAD photon-accumulation video of the city of Edinburgh:

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Canada-based Cedar Lane Technologies sues Huawei over infringement on 7 US patents, 3 of which describe image sensor data transmission schemes: 6,473,527; 6,972,790; and 8,537,242.

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Smartsens talks about its strategical move to automotive imaging market:

"Autonomous Driving presents both challenges and new opportunities for the CIS industry in China. The current shipments of automotive chips show that the gap between domestic and foreign semiconductor companies remains wide and presents an ongoing challenge for Chinese companies. It is, however, an opportunity for SmartSens.

We believe that SmartSens’ strengths in the field of security system can create an advantage in moving into the automotive industry by providing superior night vision imaging performance combined with other in-vehicle electronics technologies such as LED flicker suppression technology, and PixGain HDR technology, just to name a few. In addition, SmartSens recently acquired Shenzhen-based Allchip Microelectronics, positioning us perfectly in research and development for the next-generation automotive sensor technology.

“In the past, the semiconductor business in China relied heavily on overseas technology and research. With the rise of the local semiconductor development and the maturity of domestic CIS technology in recent years, however, we are seeing a seismic shift towards China and Asia,” said Mr. [James Ouyang, the newly appointed Deputy General Manager at SmartSens.]"

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BusinessWire: Blackmagic Design announces URSA Mini Pro 12K digital film camera with a 12,288 x 6,480 12K Super 35 image sensor, 14 stops of DR and 60 fps frame rate in 12K at 80MP per frame.

"The Blackmagic URSA Mini Pro 12K features a revolutionary new sensor with a native resolution of 12,288 x 6480, which is an incredible 80 megapixels per frame. The Super 35 sensor has a superb 14 stops of dynamic range and a native ISO of 800. The new 12K sensor has equal amounts of red, green and blue pixels and is optimized for images at multiple resolutions. Customers can shoot 12K at 60 fps or use in-sensor scaling to allow 8K or 4K RAW at up to 110 fps without cropping or changing their field of view."

The brand ambassador John Brawley shares his knowledge in cinematographers mailing list:

• Brand new sensor, 3 years in the making.
• 79 MP.
• Native 800 iso.
• 14 stops (that’s probably a bit conservative, they haven’t been able to properly check it because the models are based on Bayer sensors...:-)
• It’s not Bayer, but it has a very small pixel pitch of 2.2 microns. (Alexa is 8)
• Instead of Bayer 2x2 grid of GRBG it has a 6x6 grid. 6G, 6B and 6R plus 18 W pixels.
• The W are clear or “white” pixels. This overcomes the reduced sensitivity issue of a 2.2 micron pitch.

The sensors are shown in Blackmagic presentation video:


The new sensor's readout speed is

12K (full FOV) : ~15.5ms
8K/4K (full FOV) : ~8.5ms*
6K crop : ~7.8ms
4K crop : ~4.25ms*

*Blackmagic hopes to improve this slightly in an update

Thanks to PF and others for the pointer!

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Yole Developpement publishes an interview with Tehzeeb Gunja, Director of Medical Marketing at OmniVision. Few quotes and slides:

"With more than 500 customers and approximately 600 active projects, OmniVision possesses deep knowledge of the medical industry, and strong connections to all leading ecosystem partners and end customers globally.

Technological advancements will also continue to drive disposable adoption. CMOS imagers continue to shrink, which will allow endoscopes with smaller ODs to be designed using chip-on-tip technology. Wafer-level modules will also support large optical format imagers, thus enabling disposable, 1080p resolution for the larger OD endoscopes used in gastrointestinal and laparoscopic procedures. Additionally, there is a growing trend toward multimodal imaging and diagnosis, where the imager is used to position an ultrasound or OCT probe inside the body.

The extremely small size of newer imagers makes it feasible to be integrated directly into endoscopic tools, allowing direct line-of-sight visualization. Additionally, there is growing interest in a range of applications beyond white-light endoscopy, including the use of ultraviolet and near infrared light for fluorescence, chromo-endoscopy and virtual endoscopy. There are also novel endoscopic applications that are moving toward mainstream adoption, including narrow band imaging, multispectral imaging and light polarized imaging, among others."

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Stanford University, University of Manchester, and IBM Research in Zurich publish a paper "Neural Sensors: Learning Pixel Exposures for HDR Imaging and Video Compressive Sensing With Programmable Sensors" by Julien N.P. Martel, Lorenz K. Mueller, Stephen J. Carey, Piotr Dudek, and Gordon Wetzstein.

"Camera sensors rely on global or rolling shutter functions to expose an image. This fixed function approach severely limits the sensors’ ability to capture high-dynamic-range (HDR) scenes and resolve high-speed dynamics. Spatially varying pixel exposures have been introduced as a powerful computational photography approach to optically encode irradiance on a sensor and computationally recover additional information of a scene, but existing approaches rely on heuristic coding schemes and bulky spatial light modulators to optically implement these exposure functions. Here, we introduce neural sensors as a methodology to optimize per-pixel shutter functions jointly with a differentiable image processing method, such as a neural network, in an end-to-end fashion. Moreover, we demonstrate how to leverage emerging programmable and re-configurable sensor–processors to implement the optimized exposure functions directly on the sensor. Our system takes specific limitations of the sensor into account to optimize physically feasible optical codes and we demonstrate state-of-the-art performance for HDR and high-speed compressive imaging in simulation and with experimental results."


Thanks to PD for the link!

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Yole Developpement publishes "The CMOS image sensor market stands firm during the pandemic – Live Market Briefing" by Pierre Cambou:

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Benewake LiDAR is used to check toilet occupancy in an airport:

"In order to save time for passengers to use the toilet and reduce congestion in public toilets, some time ago, "Urumchi Diwopu International Airport" in China adopted Benewake LiDAR (TF-Luna). The use of TF-Luna can detect the toilet traffic and remaining squatting space. Both data can be displayed on the screen outside the toilet. This system solution not only relieves the congestion of public toilets, but also saves the time for users to select toilets, and plays a significant role in improving the utilization rate of public toilets and passenger satisfaction."


EETimes reporter Junko Yoshida publishes an article about Ouster LiDAR internals:

"In an interview with EE Times last week, Ouster’s founder and CEO Angus Pacala boasted that his company has already picked up 700 design wins over 15 different industries in 50 countries.

Impressive, but where’s Outster’s advantage?

Pacala said, “We chose technology designed to work in many markets.” Ouster has developed a lidar platform built on “all-CMOS semiconductors.” That makes Ouster’s products “digital lidars,” according to Pacala.

Ouster’s competitors, including Velodyne and Waymo, deploy hundreds of off-the-shelf discrete components to make their spinning lidars work. In contrast, Ouster has developed tightly integrated custom vertical cavity surface emitting lasers (VCSELs) and another ASIC that incorporates single photon avalanche diodes (SPADs) arrays.

Ouster’s platform also includes Xilinx’s FPGA, responsible for processing massive amount of data."


Quanergy unveils the MQ-8 3D LiDAR and perception software which are part of Quanergy’s Flow Management platform. Designed with a new smart beam configuration, the MQ-8 solution delivers up to 140 m continuous tracking range, enabling up to 15,000 m2 coverage with a single sensor for flow management applications like security, smart city, social distancing and smart space industries.


EPIC Online Technology Meeting on ADAS and Autonomous Driving has Ibeo and Continental LiDARs presentations:

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trinamiX introduces a novel technology called Beam Profile Analysis to measure distance and, simultaneously, obtain material features from projected laser spots. At the core of the technology is a new class of algorithms, which provides features derived from the analysis of the two-dimensional intensity distribution of each projected spot. These features correlate with distance and material properties and can be further processed by machine-learning approaches on mobile, embedded or PC type platforms.. A Beam Profile Analysis module can be built from components available at scale and consists of a standard CMOS camera and a dot projector.

"Beam Profile Analysis uses that spot shape using physically inspired features to directly measure distance and material information. Among the most important physical properties are the specifics of diffuse scattering (Lambertian scattering, volume scattering), laser speckles and lens convolutional properties (for example, several kinds of aberrations). In other words, Beam Profile Analysis consists of a recipe for specific periodic laser projection grids and a collection of specifically derived filter kernels and functions thereof to extract both distance and material classes."

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Researchers at Holst Centre have combined the organic NIR PD with an oxide thin-film transistor backplane and a focusing lens to create an NIR image sensor measuring 2.4 x 3.6 cm, large enough to image the palm of a hand or multiple fingers at a distance. It's 500-ppi resolution is state-of-the-art for biometric image sensors, enabling high-quality images of the vein pattern. In addition, the sensor achieves external QE (EQE) of 40% at 940 nm and a dark current of around 10e-6 mA/cm2.

"Together with a NIR light source, the prototype image sensor opens the door to contactless biometric security through vein pattern detection. Our thin-film technologies make for extremely thin and potentially flexible sensors that could be easily integrated into existing displays and things like mobile phones or cash machine screens, eliminating the need for separate ID and credit cards," says Daniel Tordera, Senior Scientist at Holst Centre.

Having demonstrated the potential of large-area NIR sensors for vein detection, Holst Centre is continuing to refine the technology and push its sensitivity deeper into the NIR region. With PDs efficient up to 1100 nm, these latest developments could enable new applications such as eye tracking, quality control in food production, condition monitoring of pipes and non-invasive in-body medical imaging including large area oxygen saturation (SpO2) measurements, conformable optical brain scans and cuffless blood pressure monitoring.

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Fujifilm develops a new multispectral camera based on polarization-sensing image sensor:

• High-performance multispectral camera system is equipped with a lens fitted with newly-developed filters, a polarization image sensor that can capture specific directional polarization image, and a cutting-edge image processing function. This system can simultaneously record images of different wavelength ranges in high definition and presenting them in real time.
• The newly-developed filters serve as “polarizer” that lets light in a specific direction of polarization pass through as well as “optical bandpass filter” that passes light of a specific wavelength range. The system uses three filters to split light into up to nine wavelength bands, while also polarizing the light of each wavelength band into a specific oscillation direction. (Figure 1)
• The polarization information of light in each wavelength band that has passed through the filters is recorded by the polarization image sensor and applied with the cutting-edge image processing function for visual presentation in high resolution and at a high frame rate (Figure 2). The system also allows users to choose an optical bandpass filter of the optimum wavelength band for their monitoring object.

Once we are at polarization sensing devices, OSA Image of the Week shows a nice visualization of mechanical stresses in plastic cutlery:

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ResearchInChina publishes its summary of ADAS/AD approaches of different car manufactures. Some of them rely mostly on cameras and radars, while others use many LiDARs:

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NHK has developed a three-layer color image sensor using organic films that detect only blue and only green light, layered vertically over a CMOS image sensor that detects red light.

"Incident light passes the first organic layer, which absorbs only the blue light component and converts to an electrical signal, and is transparent to the green and red components. The second organic layer absorbs only the green component, and the red component is detected by the CMOS image sensor. The organic layers are combined with transparent thin-film transistors, and the signals output from each of the layers can be combined to reproduce a color image.

This structure enables all color information of red, green and blue to be obtained within a single pixel, achieving a high-resolution image sensor that uses light more efficiently. We will continue to work reducing the pixel size and increasing the number of pixels, and accelerate R&D toward realizing a compact, high-resolution, single-chip camera."

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GizmoChina quotes a rumor that the next generation Kospet Prime 2 smartwatch will have a 3D camera. "The new sensor will improve the smartwatch’s camera support and will aid in measuring the size of any object more accurately."

The current generation Kospet Prime smartwatch released earlier this year already has a dual 5MP+2MP camera with software upscaling to 8MP:


TheElec updates on Samsung-Corephotonics patent infringement lawsuit against Apple camera module supplier LG Innotek:

"At their latest hearing on Friday at Seoul Central District Court, when asked by the judge on whether they have an outside appraiser to suggest, a LG InnoTek representative said they had someone in mind from overseas but not in South Korea and said they will look for one. LG also said that the patent invalidation judgment was expected at the end of August and asked the next hearing to be set in September.

The trial has been effectively put on hold for the past eight months due to the issue of choosing an appraiser to evaluate the patent in question.

Corephotonics filed the lawsuit back in November, 2018, alleging that LG InnoTek violated its patents related to small field of view lens assembly. LG countered with its own request to South Korea’s patent office to invalidate the patents in question on June, 2019."

Toshiba proposes a 3D camera based on a different blur for objects closer and farther than its focal plane:

"Until now, it was considered theoretically too difficult to measure distance based on the shape of the blur, which is the same for objects both near and far when they are equidistant from the focal point (Figure 3). However, analytical results revealed a substantial difference between the blur shapes of near and far objects, even when equidistant from the focal point (Figure 4). With that, Toshiba successfully analyzed blur data from captured images by a deep learning module trained with a deep neural network model.

When light passes through a lens, the shape of the blur created is known to change depending on the light's wavelength and its position in the lens. In the developed network, position and color data are processed separately to properly perceive changes in blur shape, and then, after passing through a weighted attention mechanism, are used to control where on the brightness gradient to focus in order to correctly measure the distance (Figure 5). Through learning, the network is then updated to reduce any error between the measured distance and actual distance.

Using this AI module, Toshiba has confirmed that a single image captured with a commercially available camera realizes the same distance measurement accuracy secured with stereo cameras.

Toshiba will confirm the versatility of the system with commercially available cameras and lenses and speed up the image processing, aiming for public implementation in fiscal year 2020."

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Toshiba announces a high-resolution, long-range technology for flash LiDAR, probably the next version of one presented at ISSCC 2020. At its heart is Toshiba’s compact, high-efficiency silicon photo-multiplier (SiPM).

In general, SiPM are suitable for long-range measurement as they are highly light sensitive. However, the light-receiving cells composed on SiPM require recovery time after being triggered, and in strong ambient light condition they also need a large number of cells, since they must have reserve cells to react to reflected laser light.

Toshiba’s SiPM applies a transistor circuit that reboots the cells to reduce the recovery time. The cells function more efficiently and fewer are needed, securing a smaller SiPM, as shown in Figure 1. This realizes a higher-resolution SiPM array while maintaining high sensitivity, as shown in Figures 2 and 3.


Field trials with a LiDAR prototype, shown in Figure 4, using commercially available lenses, from wide-angle to telephoto lenses, have demonstrated the system’s effectiveness over a maximum distance of 200m (Figure 5).

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SK Hynix publishes an article "Accelerated Multi-Camera Competition in Smartphone Market: Will Mobile-Centric CIS Demand Continue to Grow?" written by Yuak Pak, analyst at KIWOOM Securities. The analyst presents his view on the competition with Sony, among other things:

"For CIS, more demand for 12-inch wafers is being seen as focus shifts from 8-inch wafers. In addition, as the number of pixels increases to more than 40 million, the process started to move from 90nm to 32nm or less.

In particular, the manufacturing process of CIS is very similar to that of DRAMs, and the trench technology of DRAMs is applied to the process of high-pixel products. Therefore, it is highly likely that DRAM manufacturers will have a cost advantage over time.

SK hynix is actually applying DRAM trench process technology to eliminate light interference between pixels, while several experiments are underway to prevent the absorption of photons when using metal partition walls. In addition, ISOCELL of Samsung Electronics has also adopted DRAM process technology and is making efforts to refine their process to a 32nm level.

SK hynix, and Samsung Electronics, two of the world’s leading semiconductor memory manufacturers, are expected to close the technological gap with leading CIS players such as Sony thanks to their superior DRAM technology.

SK hynix is currently operating 8- and 12-inch CIS lines. This year’s capacity of the 12-inch CIS line has increased by more than 60% compared to last year. In addition, since some lines are being redeployed for CIS, actual performance is expected to become more visible from the end of this year.

Samsung Electronics is also expanding the capacity of its 12-inch line in addition to its existing 8-inch CIS line, mainly by utilizing old DRAM lines. Starting with 11 lines in 2018, the company is planning to convert 13 lines into a CIS line in 2020.

The industry’s number one CIS manufacturer Sony continues to increase its 12-inch capacity in Japan, which is expected to intensify the competition for market share from the second half of this year. While Sony has to construct a new line, SK hynix and Samsung Electronics are converting and deploying existing DRAM lines, which would make their products highly competitive at cost, advantaging them in competition to secure market share."

The article is complemented by the CIS market data from KIWOOM Securities:


"As of 2018, the share of demand for CIS by major industries shows the mobile industry dominating at an unbeatable 68% – followed by compute (9%), consumer (8%), security (6%), automotive (5%), and industrial (4%). In the future, this demand is expected to grow mainly within the mobile, automotive, and industrial sectors, while the market’s size – which was valued around USD 13.7 billion in 2018 – is expected to increase to USD 19 billion by 2022.

In addition to this, packaging technology integrating CIS, ISP, and DRAM is now being introduced to ultra-high-speed cameras, which is proving to be a beneficial change for companies producing both DRAMs and CIS in the mid to long term."

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Image Algorithmics kindly sent me a presentation with the company view on RGBW CFA advantages:

"There is a strong preconception in the market that RGBW does not work well. This is understandable given the failure of previous attempts. What's worse, many engineers now believe that it fundamentally cannot work well. I am attaching a slide deck to address this misconception.

We have tested our algorithms on 0.8u, 1.0u, 1.12u and 2.8u RGBW sensors. RGBW has a 6dB+ SNR advantage over Bayer in low light, read noise limited conditions and 3dB+ SNR advantage over Bayer in bright light, shot noise limited conditions. RGBW also has a 6dB dynamic range advantage."

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Delft University of Technology and Harvest Imaging publish MDPI paper "In-Pixel Temperature Sensors with an Accuracy of ±0.25 °C, a 3σ Variation of ±0.7 °C in the Spatial Domain and a 3σ Variation of ±1 °C in the Temporal Domain" by Accel Abarca, and Albert Theuwissen.

"This article presents in-pixel (of a CMOS image sensor (CIS)) temperature sensors with improved accuracy in the spatial and the temporal domain. The goal of the temperature sensors is to be used to compensate for dark (current) fixed pattern noise (FPN) during the exposure of the CIS. The temperature sensors are based on substrate parasitic bipolar junction transistor (BJT) and on the nMOS source follower of the pixel. The accuracy of these temperature sensors has been improved in the analog domain by using dynamic element matching (DEM), a temperature independent bias current based on a bandgap reference (BGR) with a temperature independent resistor, correlated double sampling (CDS), and a full BGR bias of the gain amplifier. The accuracy of the bipolar based temperature sensor has been improved to a level of ±0.25 °C, a 3σ variation of ±0.7 °C in the spatial domain, and a 3σ variation of ±1 °C in the temporal domain. In the case of the nMOS based temperature sensor, an accuracy of ±0.45 °C, 3σ variation of ±0.95 °C in the spatial domain, and ±1.4 °C in the temporal domain have been acquired. The temperature range is between −40 °C and 100 °C."

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