Saturday, July 04, 2020

Airy3D Talk about Marketing Strategy

Paul Gallagher, VP of Strategic Marketing at Airy3D, talks about challenges and solutions in marketing of the company's 3D platform in "Episode 4: Depth Perception" of The Launch podcast.


Friday, July 03, 2020

Insight to Velodyne LiDAR Business

It's official now: Velodyne LiDAR becomes a public company through reverse merging with Graf Industrial.

"Graf Industrial Corp. (NYSE: GRAF). GRAF and Velodyne have successfully raised $150MM in equity from a group of institutional investors, subject to completion of the transaction. This transaction is being structured as a reverse merger where existing Velodyne shareholders will own the majority of the go-forward company. The funds raised will be combined with up to $117M that GRAF had already raised from its existing investors. We are targeting to finalize the combination toward the end of Q3 2020, following approval by GRAF’s shareholders. At that time, Velodyne will become a publicly-traded company and we expect to be listed on the NYSE under a new ticker symbol, VLDR."

Why should it be interesting for everybody? Because Velodyne becomes the first pure-play LiDAR company that would disclose its financial results every quarter. Now, everybody can see how LiDAR business looks from inside and dynamics of the LiDAR market. The first public disclosure is below:


Optical Readout Thermal Imager

ResearchGate publishes a paper "Design and Optical Simulation of a Sensor Pixel for an Optical Readout-Based Thermal Imager" by Ambali Odebowale and Mohamed Ramy Abdelrahman from King Saud University.

"In this paper, we present an optical design and analysis of a single pixel element detector in an optical readout-based infrared imaging system. The proposed thermal imaging system contains no readout integrated circuitry and thus can be considered as a low cost alternative to typical thermal imaging systems. In this paper, we present the design and optical simulation details for a fabry perot cavity filter (FPCF)-based sensor configuration operating in the transmission mode at 650nm and as a Long Wave Infrared (LWIR) absorber in the 8000nm-12000nm band. The temperature tuning of the FPCF resonant frequency is dependent on the thermo-optic sensitivity of its cavity layer. The performance of the FPCF sensor is considered at different cavity layer thermo-optic coefficients (TOCs) and for different thermal scene temperature variations. The proposed sensor was found to be sensitive to 25mK thermal scene temperature variations."

Thursday, July 02, 2020

SWIR Upconverting Camera

MDPI paper "Up-Conversion Sensing of 2D Spatially-Modulated Infrared Information-Carrying Beams with Si-Based Cameras" by Adrián J. Torregrosa, Emir Karamehmedović, Haroldo Maestre, María Luisa Rico, and Juan Capmany from Universidad Miguel Hernández, Spain, Universidad de Alicante, Spain, and International University of Sarajevo, Bosnia and Herzegovina proposes 1550nm imaging with Si-based sensor:

"Up-conversion sensing based on optical heterodyning of an IR (infrared) image with a local oscillator laser wave in a nonlinear optical sum-frequency mixing (SFM) process is a practical solution to circumvent some limitations of IR image sensors in terms of signal-to-noise ratio, speed, resolution, or cooling needs in some demanding applications. In this way, the spectral content of an IR image can become spectrally shifted to the visible/near infrared (VIS/NWIR) and then detected with silicon focal plane arrayed sensors (Si-FPA), such as CCD/CMOS (charge-coupled and complementary metal-oxide-semiconductor devices). This work is an extension of a previous study where we recently introduced this technique in the context of optical communications, in particular in FSOC (free-space optical communications). Herein, we present an image up-conversion system based on a 1064 nm Nd3+ : YVO4 solid-state laser with a KTP (potassium titanyl phosphate) nonlinear crystal located intra-cavity where a laser beam at 1550 nm 2D spatially-modulated with a binary Quick Response (QR) code is mixed, giving an up-converted code image at 631 nm that is detected with an Si-based camera. The underlying technology allows for the extension of other IR spectral allocations, construction of compact receivers at low cost, and provides a natural way for increased protection against eavesdropping."


"The system can be miniaturized down to a quasi-monolithic robust architecture around 4 cm3 and built at a low cost with standard commercial components, resulting lightweight, and favoring field-deployable IR eye-safe links, although it is easily extensible to the MWIR and LWIR spectral regions."

Wednesday, July 01, 2020

Yole Forecasts Gold Rush in Thermal Cameras

i-Micronews: "The Covid-19 pandemic has induced a gold rush in the thermal imaging and sensing industry. All over the world, various media outlets, smaller or larger – even media behemoths – have written pieces about this technology.

We thought that it wouldn’t be too outrageous for people to measure their body temperature frequently using a smartphone that happens to be constantly in their hands. At other times, this would sound like a niche smartphone feature. But in the new era during and after the pandemic, it could prove as a helpful tool to have.

Therefore, at the beginning of June 2020, Huawei subsidiary Honor announced the Honor Play 4 smartphone, which integrates an infrared temperature sensor. According to Honor, the infra-red (IR) detector has a measurement range of -20°C to 100°C, which is more than enough to cover the human body’s range of potential temperatures. It promises an accuracy of 0.2°C, considered to be well within fever detection requirements. This looks like a medical-grade sensor. From the photo shown here in Figure 2, we believe that there is a possibility that the detector might be the newest Melexis thermopile sensor MLX90632. The specifications also fit with the product sheet. Or at least, it could be a sensor from another manufacturer that has very similar specs with the Melexis one.

The question however, remains: Is consumerization of thermal imaging/sensing technology imminent? We would dare to answer yes, but only when it’s a simple sensing function, if only temperature is read, for example from the forehead, using a cheap, robust and qualified IR detector. Thermopile technology could work just fine. This wouldn’t differ much from usual forehead thermometers. It’s just that the measurement guidelines are slightly changed by using a smartphone. On the other hand, thermal imaging would take some time. It’s a matter of educating properly consumers on how to interpret and read a thermal image. People might not be ready yet, and costs for this technology to reach the masses for daily use might still be high. Nevertheless, thermal imaging and sensing technology can surely continue to be, among others, one line of defense against Covid-19, regardless of implementation.
"

Smartphone 3D Sensing Modules Comparison

SystemPlus Consulting publishes "Smartphone 3D Sensing Modules Comparison 2020."

"The consumer 3D sensing module market is expected to reach $8.1B in 2025 from $2B in 2019, according to the “3D Imaging & Sensing 2020” report from Yole Développement. The main driver technologies are Time-of-Flight (ToF) for photography enhancement and Structured Light (SL) for facial recognition. From 2016 to 2019, a total of 22 smartphones integrating a 3D sensing module have been released, 13 with SL and 9 with ToF.

In this dynamic context, System Plus Consulting provides a deep comparative review of technology and cost of 11 3D sensing modules found in flagship smartphones, with a focus on Vertical Cavity Surface Emitting Lasers (VCSELs) and Near Infra-Red CMOS Image Sensors (NIR CIS).
"

Qualcomm Smartwatch Platform Supports 16MP Camera with 1080p30 Video

Qualcomm announces Snapdragon Wear 4100 smartwatch platform that features dual ISP with support of 16MP camera with 1080p30 video:

Tuesday, June 30, 2020

Assorted News: Brookman, Smartsens, AIStorm, Cista, Prophesee, Unispectral, SiLC, Velodyne, Himax

Brookman demos the absence of interference between its 4 pToF cameras working simultaneously:



Smartsens reports it has garnered three awards from the 2020 China IC Design Award Ceremony and Leaders Summit —co-presented by EE Times China, EDN China, and ESMC China. SmartSens won awards in three categories: Outstanding Technical Support: IC Design Companies, Popular IC Products of the Year: Sensors/MEMS, and Silicon 100.


Other imaging companies on EE Times Silicon 100 list of Emerging Startups to Watch are AIStorm, Cista Systems, Prophesee, Unispectral, SiLC


Bloomberg reports that a blank-check company Graf Industrial Corp. is in talks to merge with Velodyne Lidar in a deal that would take Velodyne public. Graf Industrial Corp. has been established in 2018 as as a blank check company with an aim to acquire one and more businesses and assets, via a merger, capital stock exchange, asset acquisition, stock purchase, and reorganization. Merging with a blank-check company has become a popular way for companies to go public, as the coronavirus pandemic upends the markets.

GlobeNewswire: Himax launches of WiseEye WE-I Plus HX6537-A AI platform that supports Google’s TensorFlow Lite for Microcontrollers.

The Himax WiseEye solution is composed of the Himax HX6537-A processor and Himax Always-on sensor. With support to TensorFlow Lite for Microcontrollers, developers are able to take advantage of the WE-I Plus platform as well as the integrated ecosystem from TensorFlow Lite for Microcontrollers to develop their NN based edge AI applications targeted for Notebook, TV, Home Appliance, Battery Camera and IP Surveillance edge computing markets.

The processor remains in low power mode until a movement/object is identified by accelerators. Afterwards, DSP coped with the running NN inference on TensorFlow Lite for Microcontrollers kernel will be able to perform the needed CV operation to send out the metadata results over TLS (Transport Level Security) protocol to main SOC and/or cloud service for application level operation. The average power consumption for Google Person Detection example inference could be under 5mW. Additionally, average Himax Always-on sensor power consumption can be less than 1mW.

Himax WE-I Plus, coupled with Himax AoS image sensors, broadens TensorFlow Lite ecosystem offering and provides developers with possibilities of high performance and ultra low power,” said Pete Warden, Technical Lead of TensorFlow Lite for Microcontrollers at Google.

Monday, June 29, 2020

Sony Prepares Subscription Service for its AI-Integrated Sensors

Reuters, Bloomberg, Yahoo: Sony plans to sell software by subscription for data-analyzing sensors with integrated AI processor like the recently announced IMX500.

We have a solid position in the market for image sensors, which serve as a gateway for imaging data,” said Sony’s Hideki Somemiya, who heads a new team developing sensor applications. Analysis of such data with AI “would form a market larger than the growth potential of the sensor market itself in terms of value,” Somemiya said in an interview, pointing to the recurring nature of software-dependent data processing versus a hardware-only business.

Most of our sensor business today can be explained only by revenues from our five biggest customers, who would buy our latest sensors as we develop,” Somemiya said. “In order to be successful in the solution business, we need to step outside that product-oriented approach.

Customer support is currently included in the one-time price of Sony sensors. But Somemiya said Sony would provide the service via separate subscription in the future. Made-by-Sony software tools would initially focus on supporting the company’s own sensors and the coverage may later expand to retain customers even if they decide to switch to non-Sony sensors, he added.

We often get queries from customers about how they can use our exotic products such as polarization sensors, short-wavelength infrared sensors and dynamic vision sensors,” Somemiya said. “So we offer them hands-on support and customized tools.

Sony will seek business partnerships and acquisitions to build out its software engineering expertise and offer seamless support anywhere in the world. Somemiya said the sensor unit’s subscription offering is a long-term plan and shouldn’t be expected to become profitable anytime soon, at least not at meaningful scale.


Sunday, June 28, 2020

LFoundry Data Shows that BSI Sensors are Less Reliable than FSI

LFoundry and Sapienza University of Rome, Italy, publish an open source paper in IEEE Journal of the Electron Devices Society "Performance and reliability degradation of CMOS Image Sensors in Back-Side Illuminated configuration" by Andrea Vici, Felice Russo, Nicola Lovisi, Aldo Marchioni, Antonio Casella, and Fernanda Irrera. The data shows that BSI sensors' lifetime in a specific discussed failure mechanism is 150-1,000 times shorter than FSI. Of course, there can be many other failure sources that mask this huge difference.

"We present a systematic characterization of wafer-level reliability dedicated test structures in Back-Side-Illuminated CMOS Image Sensors. Noise and electrical measurements performed at different steps of the fabrication process flow, definitely demonstrate that the wafer flipping/bonding/thinning and VIA opening proper of the Back-Side-Illuminated configuration cause the creation of oxide donor-like border traps. Respect to conventional Front-Side-Illuminated CMOS Image Sensors, the presence of these traps causes degradation of the transistors electrical performance, altering the oxide electric field and shifting the flat-band voltage, and strongly degrades also reliability. Results from Time-Dependent Dielectric Breakdown and Negative Bias Temperature Instability measurements outline the impact of those border traps on the lifetime prediction."


"TDDB measurements were performed on n-channel Tx at 125C, applying a gate stress voltage Vstress in the range +7 to +7.6V. For each Vstress several samples were tested and the time-to-breakdown was measured adopting the three criteria defined in the JEDEC standard JESD92 [21]. For each stress condition, the fit of the Weibull distribution of the time-to-breakdown values gave the corresponding Time-to Failure (TTF). Then, the TTFs were plotted vs. Vstress in a log-log scale and the lifetime at the operating gate voltage was extrapolated with a power law (E-model [22]).

NBTI measurements were performed on p-channel Tx at 125C, applying Vstress in the range -3 to -4V. Again, several Tx were tested. Following the JEDEC standard JESD90 [23], in this case, lifetime is defined as the stress time required to have a 10% shift of the nominal VT. The VT shift has a power law dependence on the stress time and the lifetime value at the operating gate voltage could be extrapolated.
"


"Noise and charge pumping measurements denoted the presence of donor-like border traps in the gate oxide, which were absent in the Front-Side Illuminated configuration. The trap density follows an exponential dependence on the distance from the interface and reaches the value 2x10e17 cm-3 at 1.8 nm. Electrical measurements performed at different steps during the manufacturing process demonstrated that those border traps are created during the process loop of the Back-Side configuration, consisting of wafer upside flipping, bonding, thinning and VIA opening.

Traps warp the oxide electric field and shift the flat-band voltage with respect to the Front-Side configuration, as if a positive charge centroid of 1.6x10e-8 C/cm2 at 1.7 nm was present in Back-Side configuration, altering the drain and gate current curves.

We found that the donor-like border traps affect also the Back-Side device long term performance. Time Dependent Dielectric Breakdown and Negative Bias Temperature Instability measurements were performed to evaluate lifetime. As expected, the role of border traps in the lifetime prediction is different in the two cases, but the reliability degradation of Back-Side with respect to Front-Side-Illuminated CMOS Image Sensors is evident in any case.
"

Update: Here is comment from Felice Russo:

The following comments intend to clarify the scope of the paper “Performance and reliability degradation of CMOS Image Sensors in Back-Side Illuminated configuration”.

The title reported in the Image Sensor Blog, “LFoundry Data shows that BSI Sensors are Less Reliable than FSI”, leads to a conclusion different from the intent of the authors. The purpose of the paper was to evaluate potential reliability failure mechanisms, intrinsic to a particular BSI process flow, rather than highlighting a general BSI reliability weakness. BSI sensors produced at LFoundry incorporate numerous process techniques to exceed all product reliability requirements.

It is widely accepted [Ref.1-3] that the BSI process is sensitive to charging effects, independent of the specific process flow and production line. It may cause an oxide degradation, mainly related to the presence of additional distributions of donor-like traps in the oxide, located within a tunneling distance from the silicon-oxide interface (border/slow traps) and likely linked to an oxygen vacancy.

The work, published by the University, was based on wafer level characterization data, collected in 2018 using dedicated test structures fabricated with process conditions properly modified to emphasize the influence of the main BSI process steps on the trap generation.

To address these potential intrinsic failure mechanisms, several engineering solutions have been implemented to meet all reliability requirements up to automotive grade. Our earlier published work, [Ref.4], shows BSI can match FSI TDDB lifetime with the properly engineered solutions. Understandably not all solutions can be published.

Results have been used to further improve the performance of BSI products and to identify subsequent innovative solutions for the future generations of BSI sensors.

References:
[1] J. P. Gambino et al., “Device reliability for CMOS image sensors with backside through-silicon vias”, in Proceedings of the IEEE International Reliability Physics Symposium (IRPS), 2018
[2] Lahav et al., “BSI complementary metal-oxide-semiconductor (CMOS) imager sensors”, in High performance Silicon Imaging, Second Edition, Edited by D. Durini, 2014
[3] S. G. Wuu et al., “A manufacturing BSI illumination technology using bulk-Si substrate for Advanced CMOS Image sensors”, in Proceedings of the International Image Sensor Workshop, 2009
[4] A Vici et al., “Through-silicon-trench in back-side-illuminated cmos image sensors for the improvement of gate oxide long term performance,” in Proceedings of the International Electron Devices Meeting, 2018.