Tuesday, May 26, 2020

Panasonic Paper on SPAD CMOS Sensor

Panasonic publishes MDPI paper "Modeling and Analysis of Capacitive Relaxation Quenching in a Single Photon Avalanche Diode (SPAD) Applied to a CMOS Image Sensor" by Akito Inoue, Toru Okino, Shinzo Koyama, and Yutaka Hirose. This paper opens a Special Issue on Photon Counting Image Sensors.

"We present an analysis of carrier dynamics of the single-photon detection process, i.e., from Geiger mode pulse generation to its quenching, in a single-photon avalanche diode (SPAD). The device is modeled by a parallel circuit of a SPAD and a capacitance representing both space charge accumulation inside the SPAD and parasitic components. The carrier dynamics inside the SPAD is described by time-dependent bipolar-coupled continuity equations (BCE). Numerical solutions of BCE show that the entire process completes within a few hundreds of picoseconds. More importantly, we find that the total amount of charges stored on the series capacitance gives rise to a voltage swing of the internal bias of SPAD twice of the excess bias voltage with respect to the breakdown voltage. This, in turn, gives a design methodology to control precisely generated charges and enables one to use SPADs as conventional photodiodes (PDs) in a four transistor pixel of a complementary metal-oxide-semiconductor (CMOS) image sensor (CIS) with short exposure time and without carrier overflow. Such operation is demonstrated by experiments with a 6 ┬Ám size 400 × 400 pixels SPAD-based CIS designed with this methodology."

ST Unveils ToF Sensor for Multi-Object Ranging

STMicro extends its FlightSense ToF sensors with the VL53L3CX device featuring histogram algorithms that allow measuring distances to multiple objects as well as increasing accuracy.

The VL53L3CX measures object ranges from 2.5cm to 3m, unaffected by the target color or reflectance, unlike conventional infrared sensors. This allows designers to introduce powerful new features to their products, such as enabling occupancy detectors to provide error-free sensing by ignoring unwanted background or foreground objects, or reporting the exact distances to multiple targets within the sensor’s field-of-view.

The ST patented histogram algorithms increase cover-glass crosstalk immunity and allow real-time smudge compensation preventing external contamination from adversely affecting the ranging accuracy of, for example, vacuum cleaners or equipment that may be used in a dusty industrial environment. Ranging under ambient lighting is also improved.

In addition, the VL53L3CX has high linearity that increases short-distance measurement accuracy enhancing wall tracking, faster cliff detection, and obstacle avoidance in equipment such as service robots and vacuum cleaners, markets in which ST has already enjoyed considerable commercial success. Like all FlightSense sensors, the VL53L3CX features a compact, all-in-one package design that eases integration in customer devices, as well as low power consumption that helps extend battery runtime.

The VL53L3CX is available now, priced from $1.70.

Adafruit introduces the new ST sensor:

ADAS Cameras Overview

Amkor, a packaging company, publishes "A Look Inside ADAS Modules" on various camera configurations found in different cars:

Monday, May 25, 2020

Online Training on Color Pipeline of a Camera

Framos announces an "Online Training: Colour Pipeline of a Camera" by be delivered by Albert Thuwissen on July 6-7, 2020.

The training will start with a short overview of the sensor and the lens, and will then dive into the details of a “standard” colour pipeline that is used to make a colour image out of the raw sensor signal. The following topics will be discussed:
  • Auto White Balancing: The human eye is adapting easily and quickly to the spectrum of a light source, the image sensors do not adapt at all!
  • Lens-Vignetting: Lenses have a strong fall-off of intensity and sharpness towards the edges. On top of that, also the image sensor will add an extra fall-off of intensity. Is correction possible?
  • Colour Matrixing: Nobody is perfect, neither are the imagers that suffer from optical cross-talk and from imperfections when it comes to the transmission characteristics of the colour filters. Colour matrixing takes care about these issues. Question is how to find to optimum correction matrix coefficients?
  • Contouring: This is a technique to „regain“ details, edges and sharpness in an image. But quite often not only the details are enhanced, but the noise in the image as well.
  • Colour Interpolation: The Bayer pattern sampling is extensively used in digital imaging, but the sampling is only half of the story. The other half is the demosaicing or interpolation. Several methods will be discussed and compared with each other.
  • Dark Current Compensation: The average value of the dark current can be corrected by the use of dark reference lines/pixels. Fixed-pattern noise can be corrected by means of dark frame subtraction. How efficient are these techniques? What is their influence on signal-to-noise performance and what about temperature effects?
  • Noise Filtering: A very important issue in data processing is the filtering of any remaining noise. This can be done in a non-adaptive or an adaptive way. What are the pros and cons of the various techniques?
  • Defect Correction: How can defect pixels be corrected without any visible effect? Can similar techniques also be applied to correct defect columns?

Although not really part of the colour pipeline, the following aspects of a digital camera will be discussed in the training as well:
  • Auto-exposure: How can the data of the image sensor itself being used to optimize the exposure time of the imager?
  • Auto-focusing: How can the data of the image sensor itself being used to activate the auto-focusing function?

Facial Recognition Adoption Around the Globe

VisualCapitalist publishes a summary of facial recognition approved in different countries:

  • In the US, 59% of Americans are in favor of implementing facial recognition technology for use in law enforcement, according to a Pew Research survey.
  • The US Department of Homeland Security plans to conduct facial recognition of 97% of all air travellelrs by 2023
  • In South America, Facial Recognition is used by 92% of the countries
  • 80% of Europeans are not keen on sharing facial data with authorities

Sunday, May 24, 2020

HDR Pixels Review and Comparison

Dana Diezemann published her presentation "High Dynamic Range Imaging, A short summary" at Image Sensors Europe held in London in March 2020. Few slides from the presentation:

4 Generations of Tower GS Pixels

Tower Semiconductor posts an article on its global shutter pixels development:
  • Gen 1: Our first generation of GS pixels went into production with relatively big (around ~5um) size, about ~20e of noise and a decoupling ratio between PD and MN of around 60dB. Despite being relatively lower performing than the best-in-class CCD pixels at that time, Tower Semiconductor’s GS technology was a huge market success, mainly because of much higher supported speeds at a higher resolution, which is hard to support using CCD technology. This initiated the shift in industrial cameras from CCD to CIS technology.
  • Gen 2: Our second-generation pixels were developed during our cooperation with Intel’s first RealSense™ IR camera. Originally intended for commercial applications like gesture control and 3D rendering, we adapted the technology in 2014 for industrial applications by combining 180nm periphery with 110nm metal lines in the pixel. This innovation enabled us to offer a pixel as small as 3.6um with noise of about 3e and rejection ratio of about 65dB (for the smallest pixel).
  • Gen 3: Our third generation of GS was developed using the 110nm Cu metallization technology in our TPSCo fab in Japan. In this version we had two embedded micro-lenses, that helped focus the light on the small diode area in this pixel, and also incorporated a tungsten shield (exactly like in best in class CCD), which helped in preventing light from reaching the MN, the pixel size was reduced down to 2.7um as well as a further reduction of the noise to 2e and increase in the rejection ratio to 70dB.
  • Gen 4: Our fourth, and the latest, generation of GS pixel was announced earlier this year. It is based on our 300mm wafer 65nm light pipe technology and improved tungsten shield, further enhancing the Gen3 performance. This technology allowed us to introduce the first 2.5um GS pixel with excellent performance (references IEDM, IISW), and are currently in the final development stage on further reduction of the pixel size to 2.2um.
  • Next-gen: Looking ahead, we are already developing our next generation GS pixel which will be based on Back-Side Illumination (BSI) technology. This generation would incorporate new innovations in process integration and device design to keep the MN isolated from unwanted light while maximizing light incidence on to the photo diode.

Tower "Looking Ahead" presentation also talks about other prospective markets:

Saturday, May 23, 2020

Sigmaintell Puts Galaxycore at #1 in Units Market Share

IFNews quotes Sigmaintell's somewhat optimistic forecast on this year's smartphone camera market. Sigmaintell puts Galaxycore at #1 in terms of unit market share. Almost 1/3 of all mobile phone image sensors are made by Galaxycore:

"GalaxyCore's main product is 2 / 5M, benefiting from the strong demand for 2M sensors from the multi-camera macros and depth of field of terminal manufacturers, Galaxycore Micro performed well in the first quarter. According to data from Sigmaintell, shipments of Galaxycore Micro camera sensors (including feature phones) in the first quarter of 2020 were approximately 400 million units, an increase of approximately 164% year-on-year. After entering the second quarter, the mobile phone brands began to adjust their product strategies in April. The camera upgrade trend of products with RMB 1,000 and below has significantly slowed down. The four-camera upgrade trend has been delayed. Dual-camera and three-camera are still the main market forces. Will affect its market growth rate in the second quarter and this year."

"It is expected that the global smartphone camera sensor shipments will be about 5 billion this year, maintaining a growth rate of about 5% year-on-year.

According to data from Sigmaintell, global mobile phone camera sensor shipments were approximately 1.41 billion units in the first quarter of 2020, of which smartphone camera sensor shipments were approximately 1.29 billion units, a year-on-year increase of approximately 37%. At the same time, before the outbreak, upstream and downstream are very optimistic about the market demand for camera sensors, so many agents have large quantities of stocks at this time (about 1-2 months of inventory). Under the dual pressure of a sharp decline in demand and a large supply chain inventory, the shipment of camera sensors in the second quarter will further decline.

"ToF has gradually become the standard for high-end smartphones, and currently known applications have three main aspects: one is to assist in improving the shooting effect; the other is to realize the face unlocking function; the third is to use space ranging, 3D scanning, 3D modeling and other functions.

As we all know, since the iPhone12 series in the second half of this year has two products with ToF on the market, the four major domestic terminal manufacturers are also accelerating the development of D-ToF. From the perspective of the supply chain, chip manufacturers (including Omnivision Technology and Galaxycore, etc.) are actively increasing the development of ToF hardware and software. According to data from Sigmaintell, global ToF shipments for smartphones will be approximately 90 million units in 2020.

Sony Defines its Starvis Sensor Category

Sony publishes a short presentation explaining what sensors belong to Starvis class: