Monday, April 06, 2020

Quanergy Changes CEO, Raises More Money

BusinessWire: Quanergy appoints Kevin J. Kennedy as the company’s new CEO and secures an new funding round. Kennedy keeps serving as a senior managing director of Blue Ridge Partners, one of the Quanergy investors. "While many LiDAR companies are focused on building LiDAR solely for transportation purposes, since its inception, Quanergy has emphasized the development of its technology for multiple industries,” says Kennedy. “With this new capital, we are deepening our investment in our team and our technology and are positioned to prove the value of LiDAR for broader market applications."

BusinessWire: Louay Eldada has stepped down from his positions as Quanergy CEO and board member, effective January 13, 2020. His new role in the company is defined as "Senior Evangelist."

Analog-to-Information CMOS Sensor for Image Recognition

CEA-Leti publishes a PhD Thesis "Exploring analog-to-information CMOS image sensor design taking advantage on recent advances of compressive sensing for low-power image classification" by Wissam Benjilali.

"Recent advances in the field of CMOS Image Sensors (CIS) tend to revisit the canonical image acquisition and processing pipeline to enable on-chip advanced image processing applications such as decision making. Despite the tremendous achievements made possible thanks to technology node scaling and 3D integration, designing a CIS architecture with on-chip decision making capabilities still a challenging task due to the amount of data to sense and process, as well as the hardware cost to implement state-of-the-art decision making algorithms.

In this context, Compressive Sensing (CS) has emerged as an alternative signal acquisition approach to sense the data in a compressed representation. When based on randomly generated sensing models, CS enables drastic hardware saving through the reduction of Analog to Digital conversions and data off-chip throughput while providing a meaningful information for either signal recovery or signal processing. Traditionally, CS has been exploited in CIS applications for compression tasks coupled with a remote signal recovery algorithm involving high algorithmic complexity. To alleviate this complexity, signal processing on CS provides solid theoretical guarantees to perform signal processing directly on CS measurements without significant performance loss opening as a consequence new ways towards the design of low-power smart sensor nodes.Built on algorithm and hardware research axes, this thesis illustrates how Compressive Sensing can be exploited to design low-power sensor nodes with efficient on-chip decision making algorithms.

After an overview of the fields of Compressive Sensing and Machine Learning with a particular focus on hardware implementations, this thesis presents four main contributions to study efficient sensing schemes and decision making approaches for the design of compact CMOS Image Sensor architectures. First, an analytical study explores the interest of solving basic inference tasks on CS measurements for highly constrained hardware. It aims at finding the most beneficial setting to perform decision making on Compressive Sensing based measurements.

Next, a novel sensing scheme for CIS applications is presented. Designed to meet both theoretical and hardware requirements, the proposed sensing model is shown to be suitable for CIS applications addressing both image rendering and on-chip decision making tasks. On the other hand, to deal with on-chip computational complexity involved by standard decision making algorithms, new methods to construct a hierarchical inference tree are explored to reduce MAC operations related to an on-chip multi-class inference task. This leads to a joint acquisition-processing optimization when combining hierarchical inference with Compressive Sensing.

Finally, all the aforementioned contributions are brought together to propose a compact CMOS Image Sensor architecture enabling on-chip object recognition facilitated by the proposed CS sensing scheme, reducing as a consequence on-chip memory needs. The only additional hardware compared to a standard CIS architecture using first order incremental Sigma-Delta Analog to Digital Converter (ADC) are a pseudo-random data mixing circuit, an +/-1 in-Sigma-Delta modulator and a small Digital Signal Processor (DSP). Several hardware optimization are presented to fit requirements of future ultra-low power (≈µW) CIS design.
"

Sunday, April 05, 2020

Velodyne Moves Production Overseas, Lays Off 140 Employees

Bloomberg reports that Velodyne Lidar was sued for laying off 140 workers with one day’s notice. Velodyne was expected to provide 60 days notice, but instead told employees in a written notice they were being let go because of the pandemic. The ex-employees complaint claims that “had already begun transferring production jobs overseas beginning in the summer of 2019 and had planned to continue doing so prior to the outbreak of Covid-19.

It appears to be another indication that LiDAR Mega-factory project in San Jose does not go well. Just a year ago, David Hall, Velodyne Founder and then-CEO, said "San Jose has a large and available skilled labor force that, while not price competitive with anywhere in Asia, does a higher quality job than we would get by assembling the units elsewhere."

Silion Valley Business Journal: Velodyne is valued at about $1.8b after raising about $225M from investors including Nikon, Ford, and Baidu.

Single-Photon CMOS Pixel Using Multiple Non-Destructive Signal Sampling

MDPI paper "Simulations and Design of a Single-Photon CMOS Imaging Pixel Using Multiple Non-Destructive Signal Sampling" by by Konstantin D. Stefanov, Martin J. Prest, Mark Downing, Elizabeth George, Naidu Bezawada, and Andrew D. Holland from The Open University, UK, and European Southern Observatory, Germany, describes a 10um pixel with 0.15e- noise in 180nm process.

"A single-photon CMOS image sensor (CIS) design based on pinned photodiode (PPD) with multiple charge transfers and sampling is described. In the proposed pixel architecture, the photogenerated signal is sampled non-destructively multiple times and the results are averaged. Each signal measurement is statistically independent and by averaging, the electronic readout noise is reduced to a level where single photons can be distinguished reliably. A pixel design using this method was simulated in TCAD and several layouts were generated for a 180-nm CMOS image sensor process. Using simulations, the noise performance of the pixel was determined as a function of the number of samples, sense node capacitance, sampling rate and transistor characteristics. The strengths and limitations of the proposed design are discussed in detail, including the trade-off between noise performance and readout rate and the impact of charge transfer inefficiency (CTI). The projected performance of our first prototype device indicates that single-photon imaging is within reach and could enable ground-breaking performances in many scientific and industrial imaging applications."

Saturday, April 04, 2020

Ibeo 4D LiDAR Looks Similar to Apple iPad Pro

Ibeo presented its 4D solid-state LiDAR at EPIC World Photonics Technology Summit in San Francisco on Feb 3, 2020. It looks quite similar to the one inside Apple iPad Pro 2020, other than a much longer range of Ibeo LiDAR:

iPad Pro 2020 LiDAR:


Ibeo LiDAR:



Friday, April 03, 2020

Emberion Graphene-based SWIR Sensor Presentation

Emberion CEO Tapani Ryhanen presented the company technology at EPIC World Photonics Technology Summit 2020 held on Feb. 3 in San Francisco:


IWISS2020 Cancellation

The bi-annual International Workshop on Imaging Systems and Image Sensors (IWISS) that was supposed to be held in Tokyo, Japan in November 2020 is cancelled due to coronavirus pandemic. The next IWISS is scheduled for November 2022.

Thursday, April 02, 2020

Fraunhofer Converts IR Photons to Visible Through Quantum Entanglement

Fraunhofer IOF reports: "Bio-substances such as proteins, lipids and other biochemical components can be distinguished based on their characteristic molecular vibrations. These vibrations are stimulated by light in the mid-infrared to terahertz range and are very difficult to detect with conventional measurement techniques.

But how can information from these extreme wavelength ranges be made visible? The quantum mechanical effect of photon entanglement is helping the researchers allowing them to harness twin beams of light with different wavelengths. In an interferometric setup, a laser beam is sent through a nonlinear crystal in which it generates two entangled light beams. These two beams can have very different wavelengths depending on the crystal’s properties, but they are still connected to each other due to their entanglement.

“So now, while one photon beam in the invisible infrared range is sent to the object for illumination and interaction, its twin beam in the visible spectrum is captured by a camera. Since the entangled light particles carry the same information, an image is generated even though the light that reaches the camera never interacted with the actual object,” explains [Markus] Gräfe. The visible twin essentially provides insight into what is happening with the invisible twin.
"

Actlight Announces Array of DPDs

Yahoo, PRNewswire: ActLight announces that the Dynamic PhotoDiode (DPD) sensor array has been fabricated and passed the first set of tests.

"The development of a very performant 3D image sensor based on our patented DPD technology is a great challenge for us at ActLight," said Serguei Okhonin, ActLight Co-Founder and CEO. "Seeing the performance of the first prototypes, in particular the absence of crosstalk between pixels and the first pictures produced by the array, and also considering that prototypes were built with standard CMOS image sensors technology give us the highest level of motivation to continue to invest in this project to build the high performance 3D image sensor that exceed the market expectations in terms of precision and efficiency."

Wednesday, April 01, 2020

International SPAD Sensor Workshop Goes Virtual

Due to coronavirus pandemy, International SPAD Sensor Workshop 2020 (ISSW2020) will be run as a virtual conference on June 8-9 this year. The agenda is tightly packed with excellent presentations:

  • Charge-Focusing SPAD Image Sensors for Low Light Imaging Applications
    Kazuhiro Morimoto, Canon
  • Custom silicon technologies for high detection efficiency SPAD arrays
    Angelo Gulinatti, Politecnico di Milano
  • LFoundry: SPAD, status and perspective
    Giovanni Margutti, Lfoundry
  • Device and method for a precise breakdown voltage detection of APD/SPAD in a dark environment
    Alexander Zimmer, XFAB
  • Ge on Si SPADs for LIDAR and Quantum Technology Applications
    Douglas Paul, University of Glasgow
  • 3D-Stacked SPAD in 40/45nm BSI Technology
    Georg Rohrer, AMS
  • BSI SPAD arrays based on wafer bond technology
    Werner Brockherde, Fraunhofer
  • Planar Microlenses for SPAD sensors
    Norbert Moussy, CEA-LETI
  • 3D Integrated Frontside Illuminated Photon-to-Digital Converters: Status and Applications
    Jean-Francois Pratte, University of Sherbrooke
  • Combining linear and SPAD-mode diode operation in pixel for wide dynamic range CMOS optical sensing
    Matthew Johnston, Oregon State University
  • ToF Image Sensor Systems using SPADs and Photodiodes Simon Kennedy, Monash University
  • A 1.1 mega-pixels vertical avalanche photodiode (VAPD) CMOS image sensor for a long range time-of-flight (TOF) system
    Yukata Hirose, Panasonic
  • Single photon detector for space active debris removal and exploration
    Alexandre Pollini, CSEM
  • 4D solid state LIDAR – NEXT Generation NOW
    Unsal Kabuk, IBEO
  • Depth and Intensity LiDAR imaging with Pandion SPAD array
    Salvatore Gnecchi, OnSemi
  • LIDAR using SPADs in the visible and short-wave infrared
    Gerald Buller, Heriot-Watt University
  • InP-based SPADs for Automotive Lidar
    Mark Itzler, Argo AI
  • Custom Focal Plane Arrays of SWIR SPADs
    Erik Duerr, MIT Lincoln Labs
  • CMOS SPAD Sensors with Embedded Smartness
    Angel Rodriguez-Vasquez, University of Seville
  • Modelling TDC Circuit Perfromance for SPAD Sensor Arrays
    Daniel van Blerkom, Ametek (Forza)
  • Data processing of SPAD sensors for high quality imaging
    Chao Zhang, Adaps Photonics
  • Scalable, Multi-functional CMOS SPAD arrays for Scientific Imaging
    Leonardo Gasparini, FBK
  • Small and Smart SPAD Pixels
    Edoardo Charbon, EPFL
  • High-resolution imaging of the spatio-temporal dynamics of protein interactions via fluorescence lifetime imaging with SPAD arrays
    Simon Ameer-Beg, King's College
  • Image scanning microscopy with classical and quantum correlation contrasts
    Ron Tenne, Weizmann Institute
  • Imaging oxygenation by near-infrared optical tomography based on SPAD image sensors
    Martin Wolf, ETH Zurich
  • Raman spectroscopy utilizing a time resolving CMOS SPAD line sensor with a pulsed laser excitation
    Ilkka Nissinen, University of Oulu
  • Optical wireless communication with SPAD receivers
    Hiwa Mahmoudi, TU Wien
  • SPAD Arrays for Non-Line-of-Sight Imaging
    Andreas Velten, University of Wisconsin