Monday, December 10, 2018

Gigajot Raises $4m

Gigajot files a form on completion of $4m fundrising. Not much more info has been revealed so far.

CEA-Leti Curved Sensor Technology Flyer

CEA-Leti Pixcurve technology flyer promotes the technology:

"Pixcurve is a proof of concept, introducing Leti’s latest curving technology for various optical components, such as visible imagers, µdisplays, bolometers and IR detectors. This technology addresses companies’ growing interest in a range of curved optical components that will help them achieve higher levels of performance and compensation for optical aberrations, while minimizing the vignetting effect and enhancing field of view. It makes cameras, imagers or microdisplays even more compact and easy to assemble."

Facial Recognition Controversy

The Verge, Independent, Seattle Times: AI Now Institute consisting of Microsoft, Google and New York University employees publishes "AI Now Report 2018" talking about dangers of facial recognition for society. The group calls on governments to regulate the use of AI and facial recognition technologies before they can undermine basic civil liberties.

Microsoft President Brad Smith posted a similar message in the company's blog:

"We believe it’s important for governments in 2019 to start adopting laws to regulate this technology. The facial recognition genie, so to speak, is just emerging from the bottle. Unless we act, we risk waking up five years from now to find that facial recognition services have spread in ways that exacerbate societal issues. By that time, these challenges will be much more difficult to bottle back up.

After substantial discussion and review, we have decided to adopt six principles to manage these issues at Microsoft. We are sharing these principles now, with a commitment and plans to implement them by the end of the first quarter in 2019.
"


ACLU: Department of Homeland Security published details of a U.S. Secret Service plan to test the use of facial recognition in and around the White House. The ultimate goal seems to be to give the Secret Service the ability to track “subjects of interest” in public spaces.

Voice of America, KCRA: Atlanta International Airport, which is the Delta Airlines hub, has become the first in the US to permit passengers to use facial recognition technology to get on flights. After the first check-in, passengers can also use face recognition to pass through security and to get on the plane. Delta says the system prevents the need for travelers to present their passport up to four times during the usual check-in process.


Singapore's Changi airport, Amsterdam's Schiphol, and Aruba International Airport already offer biometric check-in and boarding capability at some gates and terminals. Airports in Japan are rolling out facial recognition boarding facilities at several airports this year. China's Hongqiao International Airport is also using facial recognition for security screening. London's Heathrow plans to start testing an end-to-end facial recognition program next year.

FedScoop: A recent NIST research says that facial recognition accuracy has improved dramatically over the last 5 years:

"The technology has undergone an “industrial revolution” that’s made certain algorithms about 20 times better at searching databases and finding matches."

Sunday, December 09, 2018

CCD Dark Current Might Have Traces of Dark Matter

In the past, pixel dark current has been used for various purposes: identifying traps and defects (dark current spectroscopy), generating random numbers, measuring temperature, forensic picture analysis, random telegraph noise analysis, etc. One could think that nothing else can be in it. However, there appears to be one more thing. A recent Fermi Lab paper examines CCD dark current for the traces of Dark Matter.

Arxiv.org paper "SENSEI: First Direct-Detection Constraints on sub-GeV Dark Matter from a Surface Run" by Michael Crisler, Rouven Essig, Juan Estrada, Guillermo Fernandez, Javier Tiffenberg, Miguel Sofo Haro, Tomer Volansky, and Tien-Tien Yu:

"The Sub-Electron-Noise Skipper CCD Experimental Instrument (SENSEI) uses the recently developed Skipper-CCD technology to search for electron recoils from the interaction of sub-GeV dark matter particles with electrons in silicon. We report first results from a prototype SENSEI detector, which collected 0.019 gram-days of commissioning data above ground at Fermi National Accelerator Laboratory. These commissioning data are sufficient to set new direct-detection constraints for dark matter particles with masses between ~500 keV and 4 MeV."

Yonit Hochberg (Hebrew University of Jerusalem) review "Direct Detection of Dark Matter" explains the detection principle (DM means Dark Matter in the slides):


The Skipper CCD used in this experiment has been presented in 2017 Arxiv.org paper "Single-electron and single-photon sensitivity with a silicon Skipper CCD" by Javier Tiffenberg, Miguel Sofo-Haro, Alex Drlica-Wagner, Rouven Essig, Yann Guardincerri, Steve Holland, Tomer Volansky, and Tien-Tien Yu. The group was able to achieve an impressive performance, such as pixel dark current of 1 electron in 3 years:

"We have developed a non-destructive readout system that uses a floating-gate amplifier on a thick, fully depleted charge coupled device (CCD) to achieve ultra-low readout noise of 0.068 e- rms/pix. This is the first time that discrete sub-electron readout noise has been achieved reproducibly over millions of pixels on a stable, large-area detector. This allows the precise counting of the number of electrons in each pixel, ranging from pixels with 0 electrons to more than 1500 electrons. The resulting CCD detector is thus an ultra-sensitive calorimeter. It is also capable of counting single photons in the optical and near-infrared regime. Implementing this innovative non-destructive readout system has a negligible impact on CCD design and fabrication, and there are nearly immediate scientific applications. As a particle detector, this CCD will have unprecedented sensitivity to low-mass dark matter particles and coherent neutrino-nucleus scattering, while astronomical applications include future direct imaging and spectroscopy of exoplanets."

Saturday, December 08, 2018

Ambient Light Rejection in SPAD-based LiDAR

MDPI Special Issue The International SPAD Sensor Workshop publishes "Background Light Rejection in SPAD-Based LiDAR Sensors by Adaptive Photon Coincidence Detection" paper by Maik Beer, Jan F. Haase, Jennifer Ruskowski, and Rainer Kokozinski from Fraunhofer Institute for Microelectronic Circuits and Systems, Duisburg, and University Duisburg-Essen.

"In this paper we present a novel method based on the adaptive adjustment of photon coincidence detection to suppress the background light and simultaneously improve the dynamic range. A major disadvantage of fixed parameter coincidence detection is the increased dynamic range of the resulting event rate, allowing good measurement performance only at a specific target reflectance. To overcome this limitation we have implemented adaptive photon coincidence detection. In this technique the parameters of the photon coincidence detection are adjusted to the actual measured background light intensity, giving a reduction of the event rate dynamic range and allowing the perception of high dynamic scenes. We present a 192 × 2 pixel CMOS SPAD-based LiDAR sensor utilizing this technique and accompanying outdoor measurements showing the capability of it. In this sensor adaptive photon coincidence detection improves the dynamic range of the measureable target reflectance by over 40 dB."

Friday, December 07, 2018

Smartsens Unveils NIR-Enhanced 4MP Sensor

PRNewswire: SmartSens announces the latest addition, a 1/1.8-inch 4MP BSI SC4210, to its SmartClarity product line of security and surveillance products.

With the rapid development of artificial intelligence and IoT, application scenarios such as smart security, smart industry and intelligent driving have become increasingly popular. For example, in the China market alone, 20 million sets of intelligent monitoring systems equipped with AI technology have been installed.

According to SmartSens, "Since SmartSens' beginning, we have always taken customer needs and industry application development as the core of our technological innovation. We launched SC4210 to meet the industry's evolving needs for emerging applications in key areas such as AI and IoT. We leveraged our extensive product development experience in CMOS image sensors to develop SC4210 -- an innovative image sensing product based on advanced BSI pixel technology."

The new SC4210 is said to have many technical advantages, including:

  • Large-size pixel structure and high sensitivity: SC4210 uses BSI pixel process to achieve a 3.0μm large-size pixel structure and a 1/1.8″ optical size, with sensitivity of up to 4800mV/Lux*s and maximum signal-to-noise ratio of 43dB.
  • Ultra-low-light performance: with SmartSens' unique pixel architecture, SC4210 is leading the market with SNR1s of 0.21 lux.
  • Wide dynamic range: SC4210 supports ultra-high dynamic range (over 100dB).
  • NIR enhancement: SC4210 nearly doubles the NIR QE in 850nm to 940nm band.
  • High frame rate: SC4210 can run at up to 60fps at full 4MP resolution.

The SC4210 CMOS sensor is aimed to applications such as professional security surveillance cameras, face recognition smart cameras, industrial cameras, high-end traffic recorders, motion cameras and video teleconferencing systems. SC4210 is now in mass production.


In an unrelated note, Chinese site wxwenku reports that Smartsense 4um BSI global shutter pixel in SC130GS sensor achieves 40% QE at 940nm wavelength:

Imec PbS QD Photodiodes for CMOS Integration

IEEE Sensors Council video "NIR Sensors Based on Photolithographically Patterned PbS QD Photodiodes for CMOS Integration" by Epimitheas Georgitzikis, Pawel Malinowski, Luis Moreno Hagelsieb, Vladimir Pejovic, Griet Uytterhoeven, Stefano Guerrieri, Andreas Süss, Celso Cavaco, Konstantinos Chatzinis, Jorick Maes, Zeger Hens, Paul Heremans, and David Cheyns from Imec:

"Colloidal quantum dots based on lead sulfide are very attractive materials for the realization of novel infrared image sensors combining low cost synthesis and processing, deposition over large area and on any substrate. This work describes the building blocks that will enable the integration of QD photodiodes on top of a CMOS ROIC. Photodetectors with high detectivity and low dark current are demonstrated. Furthermore, photolithographic patterning of the thin-film stack is introduced for the first time, showing the feasibility of high pixel pitch, opening the way towards high resolution monolithic infrared imagers."

Linear SPAD Array for HDR Imaging

IEEE Sensors Council publishes "A 128×1 Pixels, High Dynamic Range SPAD Imager in 0.18 µm CMOS Technology" presentation by Cheng Mao, Xiangshun Kong, Haowen Ma, Limin Zhang, Feng Yan, and Xiaofeng Bu from Nanjing University, China.

"SPAD imager is proposed to achieve high dynamic range image. A SPAD chip with 128×1 pixels in 0.18 µm CMOS process is presented to show the feasibility. The chip design and the image method are detailed illustrated. The experiment results show that the SPAD imager can achieve 89 dB high dynamic range image, which is about 20 dB higher than that using CCD and CMOS image sensors, showing the superiority of the proposed method."

Thursday, December 06, 2018

Continuation: Deep Neural Network Search for Better CFA and Demosaicing Algorithm

Thanks to Offline Dreams mentioned another machine learning CFA pattern optimization in the comment to my yesterday's post. "Deep Joint Design of Color Filter Arrays and Demosaicing" paper by Bernardo Henz, Eduardo S. L. Gastal, and Manuel M. Oliveira from Brazilian Instituto de Informática – UFRGS differs from the previous post in a number of ways:
  • both noisy and noiseless cases are explored
  • CFA pattern is optimized together with demosaicing algorithm
  • different CFA colors were a part of optimization too

"We present a convolutional neural network architecture for performing joint design of color filter array (CFA) patterns and demosaicing. Our generic model allows the training of CFAs of arbitrary sizes, optimizing each color filter over the entire RGB color space. The patterns and algorithms produced by our method provide high-quality color reconstructions. We demonstrate the effectiveness of our approach by showing that its results achieve higher PSNR than the ones obtained with state-of-the-art techniques on all standard demosaicing datasets, both for noise-free and noisy scenarios. Our method can also be used to obtain demosaicing strategies for pre-defined CFAs, such as the Bayer pattern, for which our results also surpass even the demosaicing algorithms specifically designed for such a pattern."


The machine learning optimization picked quite different patterns from the Bayer CFA, both in color and in size:

Pixon Unveils ExDRA Software

PRNewswire: Pixon Imaging announces its new Extended Dynamic Range Architecture (ExDRA) - an software technique that improves low-light-level imaging in cell phone and other CMOS/CCD -based cameras. The technique utilizes charge binning, combining a full megapixel image of bright objects with a higher-sensitivity binned image of faint objects, all performed in one frame time. ExDRA delivers a low light performance that is an order of magnitude (or better) than current methods.

For example, Google's new Pixel 3 "Night Sight", employs up to 15 images to improve low-light sensitivity. This mimics the sensitivity of a longer exposure, but requires stationary objects, and cannot approach the sensitivity of the algorithmically simpler ExDRA. Alternative performance improving methods employed by other cell phone manufacturers require the use of multiple cameras, adding hardware costs and software complexity.

"The ExDRA technique captures both the high-resolution and high-sensitivity images simultaneously from a single CMOS/CCD sensor," reports Rick Puetter, Pixon's Chief Scientist. "This makes it possible to produce images and videos of scenes in low light with exceptional clarity and uniformity."

ExDRA is comprised entirely of software and is suited for integration into mobile phone cameras. It can also be implemented as an App, with little lead time, for use with existing devices.