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2024 IEEE International Solid-State Circuits Conference (ISSCC) will be held February 18-22, 2024 in San Francisco, CA.

Topics of interest: https://www.isscc.org/topics-of-interest

The ISSCC 2024 Conference Theme is “ICs FOR A BETTER WORLD”
ISSCC is in its 71st year as a flagship conference for solid-state circuit design. ISSCC promotes and shares new circuit ideas with the potential to advance the state-of-the-art in IC design and provide new system capabilities. This year’s conference theme highlights how today’s circuit research and development can contribute to the health, sustainability, inter-connectedness and empowerment of people’s lives. New this year, ISSCC will have a dedicated track for Security in circuits and systems, with submissions selected by a dedicated Security subcommittee. In addition, ISSCC has seen huge growth in submissions related to Machine Learning (ML) and Artificial Intelligence (AI) over the past four years, and we expect this to continue. ML- and AI-related concepts and development are now pervasive throughout topics covered by many ISSCC subcommittees. Therefore, this year ML and AI submissions are being absorbed back into several of the other core subcommittees, as reflected below. We have added experts in ML and AI to these subcommittees to continue to provide expert reviews for these topics.

IMAGERS, MEMS, MEDICAL, & DISPLAYS: Image sensors; vision sensors and event-based vision sensors; automotive, LIDAR; ultrasound and medical imaging; MEMS sensor and actuators; wearable, implantable, ingestible devices; biomedical SoCs, neural interfaces and closed-loop systems; medical devices; body area networks and body coupled communication; biosensors, microarrays; machine learning and edge computing for medical and image sensors; display drivers, sensing or haptic displays; sensing, displays and interactive technologies for AR/VR.

 

Call for papers:

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Link: https://www.yolegroup.com/product/report/imaging-for-automotive-2023/

A 2022 $5.4B automotive camera market thanks to higher autonomy demand, ASP increase due to chip shortage and product mix change to higher resolutions.

Towards a 2028 $9.4B automotive camera market driven by a 10% CAGR​

The automotive camera and image sensor markets have seen substantial revenue growth due to increased demand and higher prices driven by safety regulations and the chip shortage. The camera market was $5.4B in 2022, and the image sensor market $2.2B, projected to grow at CAGRs of 9.7% and 8.7%, respectively, to $9.4B and $3.7B by 2028. Lens sets account for one-third of camera module prices, and their value is expected to grow from $1.5B to $2.8B by 2028. The total camera market will grow from 218Munits in 2022 to 402Munits by 2028, with most cameras currently having resolutions between 1.2 and 1.7 MP. Viewing cameras have the largest volume, with the 360° surround view system gaining traction. ADAS cameras will be present in 94% of cars by 2028, while in-cabin cameras for DMS and OMS will experience rapid growth. Thermal cameras could gain traction if the cost reduces, and AEB could be the best application.

 

onsemi domination: unveiling automotive imaging leaders and rising challengers​

Despite the challenges of chip shortages and the COVID-19 pandemic, the automotive imaging ecosystem remains under the influence of OEM and Tier 1 dynamics. Valeo leads the ADAS camera market and Continental the viewing camera market. Hikvision has gained traction as a major competitor, while DJI has entered the market with stereo front cameras leveraging their drone expertise. onsemi has a 40% market share in automotive image sensors, followed by Omnivision at 26%. Sony and Samsung have performed well and offer competitive pricing. Sunny Optical dominates the lens set market with a 36% market share. Mobileye maintains a strong presence in the ADAS vision processor market with 52% and is expected to solidify its position. Although traditional Tier 1 players dominate the ADAS market, there are opportunities in the in-cabin segment, particularly in the Chinese market, where privacy concerns are less pronounced.

Driving the future: evolution of automotive imaging for enhanced safety and autonomy

Automotive image sensors are evolving to meet the demands of high resolution, dynamic range, LED flicker mitigation, and field of view. ADAS camera resolution increased to 8MP in 2022, while viewing cameras range between 1 to 3MP. The resolution improvement trend will continue, driven by the need for object detection over greater distances and autonomous driving. 2D RGB cameras are still the most cost-effective solution for ADAS and autonomous driving, while technologies like 3D and thermal cameras are still expensive. 2D RGB-IR cameras are ideal for in-cabin sensing for DMS, using infrared light for driver face detection at night. Meanwhile, 3D sensors are still waiting for a killer application. Looking ahead, the fusion of the viewing and ADAS cameras is also possible, especially for short-range detection. Hybrid lens sets, combining glass and plastic lenses, are used to reduce costs in camera modules. The industry is moving towards centralized data fusion platforms.

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EMVA’s annual two-day event, where machine vision industry and academic research meet to learn from each other, get an understanding of the newest research results, of open problems from applications, learn about new and emerging application fields, and to discuss new research cooperation between industry and academics.

The upcoming forum will take place Thursday & Friday, October 12 – 13, 2023, in Wageningen, The Netherlands.

EMVA’s local partner hosting the event is the Wageningen University & Research.

Register here: https://emvf-2023.emva.b2match.io/home

Focal Topic:
Real-world Machine Vision Challenges – Coping with Variability and Uncontrolled Environments

Machine vision solutions provide great value to end-users, but also must function well in real-world environments like agriculture, environmental monitoring, industrial and medical applications. Depending on the application at hand, specific challenges arise which concern the variability of the vision task as well as possible disturbances or operational conditions like for example large varieties of disturbances, variations of the objects to be inspected or unknown camera poses.

The European Machine Vision Forum is an annual event of the European Machine Vision Association - EMVA. The aim is to foster interaction between the machine vision industry and academic research to learn from each other, discuss the newest research results as well as problems from applications, learn about emerging application fields, and to discuss research cooperation between industry and academic institutes. The overall aim is to accelerate innovation by translating new re­search results faster into practice.

The forum is directed to scientists, development engineers, software and hardware engineers, and programmers both from research and Industry.

What to expect

• Plenary sessions with carefully selected contributed and invited talks, presenting a broad variety of a focused topic of the forum.Extended coffee and lunch breaks and evening get-together for Networking.
• Teaser sessions dedicated to poster presentations as well as hardware and software demonstrations with ample room for discussions in small groups. Each participant can submit papers and posters for free and also show demos (table-top exhibition possibilities).
•  
• Each participant will receive a certificate of his participation detailing the program.

Keynotes

Seeing Objects in Random Dot Videos
Prof. Dr. Alfred M. Bruckstein
Technion IIT, Haifa, Israel

 
Contribution of Light-field Cameras to Visual Navigation
Prof. Dr. Christophe Cudel
Université de Haute-Alsace - Irimas institute - Mulhouse, France
 
Stacked Image Sensors - Path Towards New Applications in the CIS World
Prof. Dr. Albert Theuwissen
Harvest imaging, Bree, Belgium


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A recent paper in Nature invents a new technique called "HADAR": heat-assisted detection and ranging. https://www.nature.com/articles/s41586-023-06174-6

Abstract: Machine perception uses advanced sensors to collect information about the surrounding scene for situational awareness. State-of-the-art machine perception8 using active sonar, radar and LiDAR to enhance camera vision faces difficulties when the number of intelligent agents scales up. Exploiting omnipresent heat signal could be a new frontier for scalable perception. However, objects and their environment constantly emit and scatter thermal radiation, leading to textureless images famously known as the ‘ghosting effect’. Thermal vision thus has no specificity limited by information loss, whereas thermal ranging—crucial for navigation—has been elusive even when combined with artificial intelligence (AI). Here we propose and experimentally demonstrate heat-assisted detection and ranging (HADAR) overcoming this open challenge of ghosting and benchmark it against AI-enhanced thermal sensing. HADAR not only sees texture and depth through the darkness as if it were day but also perceives decluttered physical attributes beyond RGB or thermal vision, paving the way to fully passive and physics-aware machine perception. We develop HADAR estimation theory and address its photonic shot-noise limits depicting information-theoretic bounds to HADAR-based AI performance. HADAR ranging at night beats thermal ranging and shows an accuracy comparable with RGB stereovision in daylight. Our automated HADAR thermography reaches the Cramér–Rao bound on temperature accuracy, beating existing thermography techniques. Our work leads to a disruptive technology that can accelerate the Fourth Industrial Revolution (Industry 4.0) with HADAR-based autonomous navigation and human–robot social interactions.

a, Fully passive HADAR makes use of heat signals, as opposed to active sonar, radar, LiDAR and quasi-passive cameras. Atmospherical transmittance window (white area) and temperature of the scene determine the working wavelength of HADAR. b, HADAR takes thermal photon streams as input, records hyperspectral-imaging heat cubes, addresses the ghosting effect through TeX decomposition and generates TeX vision for improved detection and ranging. c, TeX vision demonstrated on our HADAR database and outdoor experiments clearly shows that HADAR sees textures through the darkness with comprehensive understanding of the scene.

Geometric texture on a light bulb can only be seen when the bulb is off, whereas this texture is completely missing when it is glowing. The blackbody radiation can never be turned off, leading to loss of texture for thermal images. This ghosting effect presents the long-standing obstruction for heat-assisted machine perception.

 

a, TeX degeneracy limits HADAR identifiability, as in the illustrative human–robot identification problem. Top inset, distinct emissivity of human (grey body) and robot (aluminium). Bottom inset, near-identical incident spectra for human (37 °C, red) and robot (72.5 °C, blue). b, HADAR identifiability (Shannon information) as a function of normalized photon number Nd02. We compare the theoretical shot-noise limit of HADAR (solid red line) and machine-learning performance (red circles) on synthetic spectra generated by Monte Carlo (MC) simulations. We also consider realistic detectors with Johnson–Nyquist noise (γ0 = 3.34e5), flicker noise (γ1N = 3.34e5) or mixed noise (γ1N = γ0 = 3.34e5). Identifiability criterion (dashed grey line) is Nd0=1. c, The minimum photon number 1/d02 required to identify a target is usually large because of the TeX degeneracy, dependent on the scene as well as the thermal lighting factor, as shown for the scene in a. Particularly, it diverges at singularity V0 = 1 and T0 = T when the target is in thermal equilibrium with the environment.

a,d, Ranging on the basis of raw thermal images shows poor accuracy owing to ghosting. b,e, Recovered textures and enhanced ranging accuracy (approximately 100×) in HADAR as compared with thermal ranging. c,f, We also show the optical imaging (c) and RGB stereovision (f) for comparison. Insets in d and e show the depth error δz in Monte Carlo experiments (cyan points) in comparison with our theoretical bound (red curve), along the dashed white lines.

 

For an outdoor scene of a human body, an Einstein cardboard cutout and a black car at night, vision-driven object detection yields two human bodies (error) and one car from optical imaging (a) and two human bodies and no car (error) from LiDAR point cloud (c). HADAR perception based on TeX physical attributes has comprehensive understanding of the scene and accurate semantics (b; one human body and one car) for unmanned decisions. Scale bar, 1 m.

 

a, It can be clearly seen that thermal imaging is impeded by the ghosting effect, whereas HADAR TeX vision overcomes the ghosting effect, providing a fundamental route to extracting thermal textures. This texture is crucial for AI algorithms to function optimally. To prove the HADAR ranging advantage, we used GCNDepth (pre-trained on the KITTI dataset)36 for monocular stereovision, as the state-of-the-art AI algorithm. Ground-truth depth is obtained through a high-resolution LiDAR. Depth metrics are listed in Table 1. We normalized the depth metrics over that of RGB stereovision. b, The comparison of normalized metrics clearly demonstrates that ‘TeX_night ≈ RGB_day > IR_night’, that is, HADAR, sees texture and depth through the darkness as if it were day.

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Digital Imaging Class Week 5 Image Sensors (A ~2hr lecture on how CCD and CMOS image sensors work).

tinyML EMEA - Christoph Posch: Event sensors for embedded edge AI vision applications
Christoph POSCH, CTO, PROPHESEE

Event-based vision is a term naming an emerging paradigm of acquisition and processing of visual information for numerous artificial vision applications in industrial, surveillance, IoT, AR/VR, automotive and more. The highly efficient way of acquiring sparse data and the robustness to uncontrolled lighting conditions are characteristics of the event sensing process that make event-based vision attractive for at-the-edge visual perception systems that are able to cope with limited resources and a high degree of autonomy.

However, the unconventional format of the event data, non-constant data rates, non-standard interfaces, and, in general, the way, dynamic visual information is encoded inside the data, pose challenges to the usage and integration of event sensors in an embedded vision system.

Prophesee has recently developed the first of a new generation of event sensors that was designed with the explicit goal to improve the integrability and usability of event sensing technology in an embedded at-the-edge vision system. Particular emphasis has been put on event data pre-processing and formatting, data interface compatibility, and low-latency connectivity to various processing platforms including low-power uCs and neuromorphic processor architectures. Furthermore, the sensor has been optimized for ultra-low power operation, featuring a hierarchy of low-power modes and application-specific modes of operation. On-chip power management and an embedded uC core further improve sensor flexibility and useability at the edge.

Teledyne Imaging: The largest camera company you have never heard of!
A special presentation by Chris Draves, of Teledyne Imaging:
Teledyne Imaging's image sensors, cameras, and imaging components have played central roles in groundbreaking projects like the Hubble Telescope, the Mars Rovers, and the James Webb Telescope. We will explore the latest industry trends in CCD and CMOS sensors, and delve into Teledyne's extensive influence on astronomy and the space program, revolutionizing the way we observe and explore the cosmos.

Chris Draves is an accomplished professional with over 20 years of experience in the scientific camera and image sensor industry. Having worked with leading brands like Princeton Instruments, Andor Technology, Fairchild Imaging, and currently Teledyne Imaging, he has held various positions in technical sales, business development, and product management. Throughout his career, Draves has provided high-performance cameras to research labs worldwide, supporting a wide range of applications. He currently resides in Madison, WI.

2023 ReThinking NewSpace Webinar - CNES - Image sensors for space applications

The CNES perspective
Image sensors are everywhere in space, these detectors are our eyes where humans won’t or can’t go because of the environment. During our session, CNES will dive into numerous missions, which capture HD-colour images providing exclusive geographic data.

CNES missions have the purpose to uncover the challenges and solutions of using visible and infrared sensors in extreme environments. Space-born image sensors and their evolutions will have a significant growing impact on the future of space exploration and how space will be commercialized.

By Valerian Lalucaa - Detection Chain Specialist
Recorded on June 13th 2023

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From Businesswire: https://www.businesswire.com/news/home/20230725606397/en/Ubicept-Raises-8M-to-Unlock-Computer-Vision-in-All-Lighting-Conditions-by-Counting-Individual-Photons

Ubicept Raises $8M to Unlock Computer Vision in All Lighting Conditions by Counting Individual Photons

Company plans to use capital to attract new talent and expand into several new industries including 3D scanning and industrial automation 

BOSTON--(BUSINESS WIRE)--Ubicept, the revolutionary computer vision technology company, today announced it has secured $8M in funding. The oversubscribed seed investment round was led by Ubiquity Ventures and E14 Fund, with participation from Wisconsin Alumni Research Foundation, Phoenix Venture Partners (PVP), and several other investors and angel contributors.

Born out of the world-class labs of MIT and University of Wisconsin-Madison, Ubicept is redefining boundaries in the field of computer vision. Traditional computer vision relies on a dated "still frame" approach, whereas Ubicept bypasses this old logic and directly leverages single-photon sensors to turn the individual photons that hit an imaging sensor into a reliable computer vision output. The resulting perception system can operate in extreme lighting conditions, capture sharp images of high-speed motion, and even "see" around corners. Ubicept targets a price point similar to conventional camera systems.

“We are excited about this major milestone. This funding will allow us to accelerate our efforts to transform the way computers 'see' and understand the world, especially in challenging environments," said Sebastian Bauer, co-founder and CEO.

"Ubicept is the first company in the world with this "count individual photons" approach to computer vision. I see tremendous demand right now for this next generation of perception and the use cases it unlocks," said Sunil Nagaraj of Ubiquity Venture. Mr. Nagaraj has also joined the Ubicept Board of Directors.

Habib Haddad, Managing Partner of E14 Fund, adds: “The development in the market for single-photon sensors has picked up dramatically in the last few years, with smartphone manufacturers adding them to their devices for depth sensing. The processing Ubicept adds to such sensor type will enable their widespread use for general-purpose imaging and a wide array of computer vision applications. The output quality is so much better than what conventional sensors provide.” The new capital will be used to expand the Ubicept team, secure further intellectual property rights, and bring their product to more customers across several industries. This investment will strengthen Ubicept's position as the leader in single-photon computer vision. https://www.ubicept.com/

About Ubicept
Ubicept is a computer vision startup spun out of the labs of MIT and UW-Madison. The company is developing advanced computer vision and image processing algorithms using single-photon sensitive image sensors that can function in extreme lighting conditions, swiftly capture motion, and even see around corners.

About Ubiquity Ventures
Ubiquity Ventures is a seed-stage institutional venture capital firm that invests in "software beyond the screen" startups and has over $150 million under management. Ubiquity's portfolio includes B2B technology companies that utilize smart hardware or machine learning to solve business problems outside the reach of computers and smartphones. By transforming real-world physical problems into the domain of software, Ubiquity startups tap into large greenfield markets and offer more effective solutions. See more details on the Ubiquity Ventures website at http://www.ubiquity.vc.

About E14 Fund
E14 Fund is the MIT-affiliated, early stage venture capital firm. E14 Fund invests in MIT deep tech startups that are transforming traditional industries across a broad array of market-ready, scalable innovations in AI/ML, robotics, climate, biomanufacturing, life sciences, material science, sensing and more, supporting its portfolio and community with resources from across the MIT ecosystem. For more information, visit www.e14fund.com.

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Yole press release July 21, 2023: CIS: Sony is still leading the market

• A 5.1% CAGR between 2022 and 2028 is announced in Yole Intelligence‘s yearly analysis. The market should reach $28.8 billion at the end of the period.
• CIS player market shares: there are no changes in the Top 5 compared to the year before. Sony is still leading the market with a 42% market share.
• The CIS industry is pushed by technological innovations linked to performance, integrability, and new sensing capabilities.

2022 has been a transition year for the CIS industry. In the Status of the CMOS Image Sensor Industry report, Yole Intelligence, part of Yole Group analysts see similar revenues to the year before and a slight decline in overall volumes. However, a significant transformation is underway in the market structure, as evident in the growth of the automotive segment and the increase in the CIS average selling price.

Florian Domengie, Senior, Technology & Market Analyst, Imaging at Yole Intelligence says:
"There is a trend for custom CMOS image sensor products for mid- and low-volume differentiated markets, including niche markets, that do not face the same performance and cost pressure as higher-volume markets such as mobile, automotive, and consumer. Currently, numerous companies are adopting this approach."

Sony is again increasing its commanding position while Omnivision has retreated to close to its pre-COVID-19 market share. Samsung also reduced its footprint, apparently to the benefit of SK hynix. Onsemi saw an exceptional 2022, boosted by the automotive and industrial markets. GalaxyCore and SmartSens have retreated, apparently due to the disinflation of the low-end mobile and security camera markets.

The economic conflict between the U.S. and China has left its mark on the geographically competitive CIS landscape. Initially, the U.S. sanctions on Huawei mainly hit Sony while boosting the Chinese CIS players. The latter then prospered thanks to domestic market opportunities in consumer, automotive, and security. However, in 2022 the bubble burst in the security market, while the U.S. efforts to hit Chinese semiconductor firms also translated into CIS suppliers.

With the slowdown in the mobile and computing market and the recent temporary drop in the security CIS market, Chinese CIS suppliers aim to decrease their exposure to these markets and gain market share in those that are thriving and deliver higher value and ASP : Automotive and industrial. In addition, the domestic market ensures a high demand for these applications.

Overall, there are ongoing investments to either secure capacity, including for logic wafer production, or develop in-house technologies as a strategic vision to get further market share.

 

 

From a market perspective, Yole Intelligence announces a return to steady growth. CIS revenues stagnated in 2022, at US$21.3 billion, in the continuity of a soft-landing situation compared to the largely inflated growth experienced in previous years. The general inflation in 2022 translated to a significant slowdown in consumer product sales, such as smartphones: Yole Intelligence’s imaging analysts estimate a 10% decrease. 

However, higher-end CIS products and new sensing opportunities will sustain the mobile CIS market in the coming years. In addition, automotive cameras are experiencing good growth enabled by in-cabin, viewing, and ADAS applications, promoted further by safety regulations.
In parallel, the share of the mobile CIS market should continue to decrease compared to the growing share of automotive, security, and industrial CIS, with the resulting product mix maintaining the overall ASP beyond US$3.

“We have adjusted downward our long-term CIS forecast, with a 5.1% revenue CAGR from 2022 – 2028, and the resulting CIS revenues should reach US$29 billion by 2028”, explains Domengie.

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Photonis announces the acquisition of El-Mul, leader in ion and electron detection solutions

Mérignac, France and Rehovot, Israel – July 19, 2023
 
Photonis a global leader of electro-optical detection and imaging technologies for defense and industrial markets, held by HLD since 2021, is pleased to announce the acquisition of Israeli company El-Mul, a specialist developer and manufacturer of advanced charged particle detectors and devices.

By welcoming El-Mul along with Xenics, Telops and Proxivision acquired in the last eight months, Photonis Group pursues its diversification and establishes itself as the sole sizeable European technology platform providing differentiated detection and imaging solutions across the electromagnetic spectrum to a variety of high-growth end markets worldwide.

“With the acquisition of El-Mul, Photonis group will gain access to the Electron Microscopy and Semiconductor inspection markets from a strong leading position, will reinforce its technology leadership in the Mass Spectrometry market and accelerate its growth into industrial and commercial markets.” Jérôme Cerisier, CEO of Photonis Group said.

El-Mul, based in Israel with 50 employees, is a well-established technology leader in the field of detection systems for Scanning Electron Microscopes for both the Analytical and Semiconductor industries as well as the field of electron and ion optics for Mass Spectrometry, having a strong position in the worldwide high-end markets.

“El-Mul has emerged as an innovative leader in electron and ion detection with the continued support of its founders and shareholders Cheifez family since 1992. Joining Photonis Group is a real opportunity to accelerate our growth. We will benefit from the group expertise, technological and commercial base, and international reach. There are also very promising synergies between our companies in terms of market, product range and R&D. Especially new R&D co-developments should bring significant added value to our customers.” Sasha Kadyshevitch, CEO of El-Mul said.

The transaction is finalized. Terms of the transaction are not being disclosed.
 
 
ABOUT PHOTONIS:
 
Accompanied by HLD since 2021, Photonis is a high-tech company, with more than 85 years of experience in the innovation, development, manufacture and sale of technologies in the field of photo detection and imaging. Today, it offers its customers detectors and detection solutions: its power tubes, digital cameras, neutron & gamma detectors, scientific detectors and intensifier tubes allow Photonis to respond to complex issues in environments extremely demanding by offering tailor-made solutions to its customers. Thanks to its sustained and permanent investment, Photonis is internationally recognized as a major innovator in optoelectronics, with production and R&D carried out on 8 sites, in Europe and the USA and over 1200 employees.

For more information: photonis.com
 
ABOUT EL-MUL
 
Since its founding in 1992, El-Mul Technologies has established itself as a leading supplier of advanced, high performance particle detectors that meet the most challenging needs of its customers. El-Mul excels in tailor-design of solutions that match customers’ requirements. Complex detection solutions which incorporate mechanical, optical and electronic components are conceived from square one through to full development, prototyping and serial manufacturing. El-Mul’s products range from traditional detection modules to state-of-the-art systems. An emphasis on innovation, confidentiality and personal service drives its business philosophy. A key strategic business goal for El-Mul is to build long-term and fruitful relationships with its customers – delivering performance, high confidence and clear value.

For more information: el-mul.com

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Edgehog is a Montreal-based startup that has developed a solution for the stray light problem in camera and LiDAR sensors' coverglass.

Edgehog glass, a next-generation anti-reflection technology, removes image artifacts through the innovative process of glass nanotexturing by creating a gradient of refractive index on filters and image sensor covers. This enables uncompromised visuals from cameras and flare-free imaging with CMOS image sensors even in challenging lighting conditions. The result is a cleaner raw signal from the hardware without expensive image processing, laying the foundation for superior computer vision applications. The advanced nanotextured Edgehog glass enables camera optics designers to achieve unparalleled image clarity for a wide viewing angle.

 

Email: info@edgehogtech.com
Phone: +1 (438) 230 0101
Web: http://www.edgehogtech.com

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Huang et al. from Shanghai Advanced Research Institute recently published a paper titled "A 13-Bit, 12-ps Resolution Vernier Time-to-Digital Converter Based on Dual Delay-Rings for SPAD Image Sensor" in Sensors journal.

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From IDQuantique: https://www.idquantique.com/sk-telecom-and-samsung-unveil-the-galaxy-quantum-4/

SK Telecom and Samsung unveil the Galaxy Quantum 4, providing more safety and performance with IDQ’s QRNG Chip

Geneva, June 12th 2023

ID Quantique (IDQ), the world leader in quantum-safe security solutions, SK Telecom and Samsung Electronics, have worked together to release the ‘Galaxy Quantum 4’, the fourth Samsung smartphone equipped with quantum technology, designed to protect customers’ information.

With features matching those of Samsung’s flagship smartphones of the S23 series – i.e. waterdrop camera with image stabilization (OIS) and nightography (night/low-light shooting), rear glass design, large capacity battery – along with strengthened quantum-safe technology, the Galaxy Quantum 4 will be a new choice for customers who value both high performance and security.

Like its predecessor, the Galaxy Quantum 4 is equipped with the world’s smallest (width 2.5mm x length 2.5 mm) Quantum Random Number Generator (QRNG) chipset, designed by ID Quantique; enabling trusted authentication and encryption of information. It allows smartphone holders to use an even wider number of applications and services in a safer and more secure manner by generating unpredictable true random numbers.

IDQ’s QRNG chip enhances the security of a very large number of services provided by the operator. QRNG protects the process from log-in/authentication/payment/unlock/OTP generation of service apps ranging from financial apps to social media apps and games offering a much higher level of trust to the users.

As an example, when an application provides authentication services, sensitive data such as fingerprints and facial images must be protected. Our QRNG, embedded in this new smartphone, can therefore be leveraged to generate encryption keys and, in conjunction with the keystore of the terminal, provide quantum enhanced security every time a user logs in to the app. The QRNG is also used to encrypt data stored in the external memory card.

As the previous version, the ‘Galaxy Quantum 4’ offers a differentiated security experience to customers by providing a ‘quantum indicator’ on the status bar so that customers can realize that they are using a quantum security service. Its price point is comparable to previous versions, but with increased performance and security.

“Protecting one’s private data is a priority for users. The Galaxy Quantum 4 is the latest in the Quantum series, which offers strong quantum security and premium performance. As a leading player in this area, we will continue to expand the use of quantum cryptography technology to provide users with greater security and safety,” said Moon Kab-in, Vice President and Head of Smart Device Center at SKT.

“Mobile phone users don’t want to get their data stolen. The Galaxy Quantum 4 includes top performances and more quantum-secured applications than ever before, bringing applications and services to a new level of security in the mobile phone industry” said Grégoire Ribordy, CEO and co-founder of ID Quantique.

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From Semiconductor Engineering https://semiengineering.com/machine-vision-plus-ai-ml-opens-huge-opportunities/

Machine Vision Plus AI/ML Adds Vast New Opportunities

Traditional technology companies and startups are racing to combine machine vision with AI/ML, enabling it to “see” far more than just pixel data from sensors, and opening up new opportunities across a wide swath of applications.

In recent years, startups have been able to raise billions of dollars as new MV ideas come to light in markets ranging from transportation and manufacturing to health care and retail. But to fully realize its potential, the technology needs to address challenges on a number of fronts, including improved performance and security, and design flexibility.

Fundamentally, a machine vision system is a combination of software and hardware that can capture and process information in the form of digital pixels. These systems can analyze an image, and take certain actions based on how it is programmed and trained. A typical vision system consists of an image sensor (camera and lens), image and vision processing components (vision algorithm) and SoCs, and the network/communication components.

Both still and video digital cameras contain image sensors. So do automotive sensors such as lidar, radar, ultrasound, which deliver an image in digital pixel form, although not with the same resolution. While most people are familiar these types of images, a machine also can “see” can heat and audio signals data, and they can analyze that data to create a multi-dimensional image.
“CMOS image sensors have seen drastic improvement over the last few years,” said Ron Lowman, strategic marketing manager at Synopsys. “Sensor bandwidth is not being optimized for human sight anymore, but rather for the value AI it can provide. For instance, MIPI CSI, the dominant vision sensor interface, is not only increasing bandwidths, but also adding AI features such as Smart Region of Interest (SROI) and higher color depth. Although these color depth increases can’t be detected by the human eye, for machine vision it can improve the value of a service dramatically.”

Machine vision is a subset of the broader computer vision. “While both disciplines rely on looking at primarily image data to deduce information, machine vision implies ‘inspection type’ applications in an industry or factory setting,” said Amol Borkar, director of product management, marketing and business development, Tensilica Vision and AI DSPs at Cadence. “Machine vision relies heavily on using cameras for sensing. However, ‘cameras’ is a loaded term because we are typically familiar with an image sensor that produces RGB images and operates in the visible light spectrum. Depending on the application, this sensor could operate in infrared, which could be short wave, medium wave, long wave IR, or thermal imaging, to name a few variants. Event cameras, which are very hyper-sensitive to motion, were recently introduced. On an assembly line, line scan cameras are a slightly different variation from typical shutter-based cameras. Most current applications in automotive, surveillance, and medical rely on one or more of these sensors, which are often combined to do some form of sensor fusion to produce a result better than a single camera or sensor.”

Benefits
Generally speaking, MV can see better than people. The MV used in manufacturing can improve productivity and quality, lowering production costs. Paired with ADAS for autonomous driving, MV can take over some driving functions. Together with AI, MV can help analyze medical images.
The benefits of using machine vision include higher reliability and consistency, along with greater precision and accuracy (depending on camera resolution). And unlike humans, machines do not get tired, provided they receive routine maintenance. Vision system data can be stored locally or in the cloud, then analyzed in real-time when needed. Additionally, MV reduces production costs by detecting and screening out defective parts, and increases inventory control efficiency with OCR and bar-code reading, resulting in lower overall manufacturing costs.

Today, machine vision usually is deployed in combination with AI, which greatly enhances the power of data analysis. In modern factories, automation equipment, including robots, is combined with machine vision and AI to increase productivity.

How AI/ML and MV interact
With AI/ML, MV can self-learn and improve after capturing digital pixel data from sensors.
“Machine vision (MV) and artificial intelligence (AI) are closely related fields, and they often interact in various ways,” said Andy Nightingale, vice president of product marketing at Arteris IP. “Machine vision involves using cameras, sensors, and other devices to capture images or additional data, which is then processed and analyzed to extract useful information. Conversely, AI involves using algorithms and statistical models to recognize patterns and make predictions based on large amounts of data.”
This also can include deep learning techniques. “Deep learning is a subset of AI that involves training complex neural networks on large datasets to recognize patterns and make predictions,” Nightingale explained. ” Machine vision systems can use deep learning algorithms to improve their ability to detect and classify objects in images or videos. Another way that machine vision and AI interact is through the use of computer vision algorithms. Computer vision is a superset of machine vision that uses algorithms and techniques to extract information from images and videos. AI algorithms can analyze this information and predict what is happening in the scene. For example, a computer vision system might use AI algorithms to analyze traffic patterns and predict when a particular intersection will likely become congested. Machine vision and AI can also interact in the context of autonomous systems, such as self-driving cars or drones. In these applications, machine vision systems are used to capture and process data from sensors. In contrast, AI algorithms interpret this data and make decisions about navigating the environment.”

AI/ML, MV in autonomous driving
AI has an increasing number of roles in modern vehicles, but the two major roles are in perception and decision making.

“Perception is the process of understanding one’s surroundings through onboard and external sensor arrays,” said David Fritz, vice president of hybrid and virtual systems at Siemens Digital Industries Software. “Decision-making first takes the understanding of the surrounding state and a goal such as moving toward the destination. Next, the AI decides the safest, most effective way to get there by controlling the onboard actuators for steering, braking, accelerating, etc. These two critical roles address very different problems. From a camera or other sensor, the AI algorithms will use raw data from the sensors to perform object detection. Once an object is detected, the perception stack will classify the object, for example, whether the object is a car, a person, or an animal. The training process is lengthy and requires many training sets presenting objects from many different angles. After training, the AI network can be loaded into the digital twin or physical vehicle. Once objects are detected and classified decisions can be made by another trained AI network to control steering, braking, and acceleration. Using a high-fidelity digital twin to validate the process virtually has been shown to result in safer, more effective vehicles faster than simply using open road testing.”

How much AI/ML is needed is a question frequently asked by developers. In the case of modern factories, MV can be used to simply detect and pick out defective parts in an assembly line or employed to assemble automobiles. Doing the latter requires advanced intelligence and a more sophisticated design to ensure timing, precision, and calculation of motion and distance in the assembly process.
“Automation using robotics and machine vision has increased productivity in modern factories,” observed Geoff Tate, CEO of Flex Logix. “Many of these applications use AI. A simple application — for instance, detecting if a label is applied correctly — does not require a great deal of intelligence. On the other hand, a sophisticated, precision robot arm performing 3D motion requires much more GPU power. In the first application, one tile of AI IP will be sufficient, while the second application may need multiple tiles. Having flexible and scalable AI IPs would make designing robotics and machine vision much easier.”

Applications
Machine vision applications are limited only by one’s imagination. MV can be used in almost any industrial and commercial segment, so long as it requires vision and processing. Here is a partial list:
 Transportation (autonomous driving, in-cabin monitoring, traffic flow analysis, moving violation and accident detection);

•  Manufacturing and automation (productivity analysis, quality management);
•  Surveillance (detection of motion and intrusion monitor);
•  Health care (imaging, cancer and tumor detection, cell classification);
•  Agriculture (farm automation, plant disease and insect detection);
•  Retail (customer tracking, empty shelf detection, theft detection), and
•  Insurance (accident scene analysis from images).

There are many other applications. Consider drinking water or soft drink bottling. A machine vision system can be used to inspect fill levels, which typically is done by highly efficient robots. But robots occasionally make mistakes. MV can ensure the fill level is consistent and the labels are applied correctly.

Detecting any machine parts that deviate from measurement specification limits is another job for MV. Once the MV is trained on the specification, it can detect the parts that are outside the specification limits.

MV can detect uniform shapes such as squares or circles as well as odd-shaped parts, so it can be used to identify, detect, measure, count, and (with robots), pick and place.
Finally, combining AI, MV can perform tire assembly with precision and efficiency. Nowadays, OEMs automate vehicle assembly with robots. One of the processes is to install the four wheels to a new vehicle. Using MV, a robotic arm can detect the correct distance and apply just the right amount of pressure to prevent any damage.

Types of MV
MV technologies can be divided into one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D).

1D systems analyze data one line at a time, comparing variations among groups. Usually it is used in production of items such as plastics and paper on a continual basis. 2D systems, in contrast, use a camera to scan line by line to form an area or a 2D image. In some cases, the whole area is scanned and the object image can then be unwrapped for detailed inspection. 

3D systems consist of multiple cameras or laser sensors to capture the 3D view of an object. During the training process, the object or the cameras need to be moved to capture the entire product. Recent technology can produce accuracy within micrometers. 3D systems produce higher resolution but are also more expensive.

Emerging MV startups and new innovations
Tech giants, including IBM, Intel, Qualcomm, and NVIDIA, have publicly discussed investments in MV. In addition, many startups are developing new MV solutions such as Airobotics , Arcturus Networks, Deep Vision AI , Hawk-Eye Innovations, Instrumental, lending AI, kinara, Mech-Mind, Megvii, NAUTO, SenseTime, Tractable, ViSenze, Viso, and others. Some of these companies have been able to raise funding in excess of $1 billion.

In transportation, insurance companies can use MV to scan photographs and videos of scenes of accidents and disasters for financial damage analysis. Additionally, AI-based MV can power safety platforms to analyze driver behavior.

In software, computer vision platforms can be created without the knowledge of coding. Other startups have developed the idea for MV authentication software. And in the field of sports, AI, vision, and data analysis could provide coaches the ability to understand how decisions are made by players during a game. Also, one startup devised a cost reduction idea for surveillance by combining AI and MV in unmanned, aerial drone design.

Both MV and AI are changing quickly, and will continue to increase in performance, including precision and accuracy, while high GPU and ML power will come down in cost, propelling new MV applications.

Arteris’ Nightingale noted there will be further improvements in accuracy and speed. “Machine vision systems will likely become more accurate and faster. This will be achieved through advancements in hardware, such as sensors, cameras, and processors, as well as improvements in algorithms and machine learning models,” he said, pointing to an increased use of deep learning, as well. “Deep learning has been a significant driver of progress in machine vision technology in recent years, and it is likely to play an even more substantial role in the future. Deep learning algorithms can automatically learn data features and patterns, leading to better accuracy and performance. There will be an enhanced ability to process and analyze large amounts of data, as machine vision technology can process and analyze large amounts of data quickly and accurately. We may also see advancements in machine vision systems that can process significantly larger datasets, leading to more sophisticated and intelligent applications.”

Further, MV and AI are expected to integrate with other technologies to provide additional high-performance, real-time applications.

“Machine vision technology is already integrated with other technologies, such as robotics and automation,” he said. “This trend will likely continue, and we may see more machine vision applications in health care, transportation, and security. As well, there will be more real-time applications. Machine vision technology is already used for real-time applications, such as facial recognition and object tracking. In the future, we may see more applications that require real-time processing, such as self-driving cars and drones.”

MV design challenges
Still, there are challenges in training an MV system. Its accuracy and performance depend on how well the MV is trained. Inspection can encompass parameters such as orientation, variation of the surfaces, contamination, and accuracy tolerances such as diameter, thickness, and gaps. 3D systems can perform better than 1D or 2D systems when detecting cosmetic and service variation effects. In other cases, when seeing an unusual situation, human beings can draw on knowledge from a different discipline, while MV and AI may not have that ability.

“Some of today’s key challenges include data flow management and control – especially with real-time latency requirements such as those in automotive applications — while keeping bandwidth to a minimum,” said Alexander Zyazin, senior product manager in Arm‘s Automotive Line of Business. “In camera-based systems, image quality (IQ) remains critical. It requires a hardware design to support ultra-wide dynamic range and local tone mapping. But it also requires IQ tuning, where traditionally subjective evaluation by human experts was necessary, making the development process lengthy and costly. The new challenge for MV is that this expertise might not result in the best system performance, as perception engines might prefer to see images differently to humans and to one another, depending on the task.”

In general, machines can do a better job when doing mundane tasks over and over again, or when recognizing an image with more patterns than humans can typically process. “As an example, a machine may do a better job recognizing an anomaly in a medical scan than a human, simply because the doctor may make a mistake, be distracted or tired,” said Thomas Andersen, vice president for AI and machine learning at Synopsys. “When inspecting high-precision circuits, a machine can do a much better job analyzing millions of patterns and recognizing errors, a task a human could not do, simply due to the size of the problem. On the other hand, machines have not yet reached the human skill of recognizing the complex scenes that can occur while driving a car. It may seem easy for a human to recognize and anticipate certain reactions, while the machine may be better in ‘simple’ situations that a human easily could deal with, but did not due to a distraction, inattention or incapacitation – for example auto stop safety systems to avoid an imminent collision. A machine can always react faster than a human, assuming it interprets the situation correctly.”

Another challenge is making sure MV is secure. With cyberattacks increasing constantly, it will be important to ensure no production disruption or interference from threat actors.

“Security is critical to ensuring the output of MV technology isn’t compromised,” said Arm’s Zyazin. “Automotive applications are a good example of the importance of security in both hardware and software. For instance, the information processed and extracted from the machine is what dictates decisions such as braking or lane-keep assist, which can pose a risk to those inside the vehicle if done incorrectly.”

Conclusion
MV designs include a mixture of chips (processors, memories, security), IPs, modules, firmware, hardware and software. The rollout of chiplets and multi-chip packaging will allow those systems to be combined in novel ways more easily and more quickly, adding new features and functions and improving the overall efficiency and capabilities of these systems.

“Known good die (KGD) solutions can provide cost and space efficient alternatives to packaged products with limited bonding pads and wires,” said Tetsu Ho, DRAM manager at Winbond. That helps improve design efficiency, provides enhanced hardware security performance, and especially time-to-market for product launch. These die go through 100% burn-in and are tested to the same extent as discrete parts. KGD 2.0 is needed to assure end-of-line yield in 2.5D/3D assembly and 2.5D/3D multichip devices to realize improvements in PPA, which means bandwidth performance, power efficiency, and area as miniaturization, driven by the explosion of technologies such as edge-computing AI.”

This will open new options for MV in new an existing markets. It will be used to support humans in autonomous driving, help robots perform with precision and efficiency in manufacturing, and perform surveillance with unmanned drones. In addition, MV will be able to explore places that are considered dangerous for humans, and provide data input and analysis for many fields, including insurance, sports, transportation, defense, medicine, and more.

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From Semiconductor Engineering https://semiengineering.com/machine-vision-plus-ai-ml-opens-huge-opportunities/

Machine Vision Plus AI/ML Adds Vast New Opportunities

Traditional technology companies and startups are racing to combine machine vision with AI/ML, enabling it to “see” far more than just pixel data from sensors, and opening up new opportunities across a wide swath of applications.

In recent years, startups have been able to raise billions of dollars as new MV ideas come to light in markets ranging from transportation and manufacturing to health care and retail. But to fully realize its potential, the technology needs to address challenges on a number of fronts, including improved performance and security, and design flexibility.

Fundamentally, a machine vision system is a combination of software and hardware that can capture and process information in the form of digital pixels. These systems can analyze an image, and take certain actions based on how it is programmed and trained. A typical vision system consists of an image sensor (camera and lens), image and vision processing components (vision algorithm) and SoCs, and the network/communication components.

Both still and video digital cameras contain image sensors. So do automotive sensors such as lidar, radar, ultrasound, which deliver an image in digital pixel form, although not with the same resolution. While most people are familiar these types of images, a machine also can “see” can heat and audio signals data, and they can analyze that data to create a multi-dimensional image.
“CMOS image sensors have seen drastic improvement over the last few years,” said Ron Lowman, strategic marketing manager at Synopsys. “Sensor bandwidth is not being optimized for human sight anymore, but rather for the value AI it can provide. For instance, MIPI CSI, the dominant vision sensor interface, is not only increasing bandwidths, but also adding AI features such as Smart Region of Interest (SROI) and higher color depth. Although these color depth increases can’t be detected by the human eye, for machine vision it can improve the value of a service dramatically.”

Machine vision is a subset of the broader computer vision. “While both disciplines rely on looking at primarily image data to deduce information, machine vision implies ‘inspection type’ applications in an industry or factory setting,” said Amol Borkar, director of product management, marketing and business development, Tensilica Vision and AI DSPs at Cadence. “Machine vision relies heavily on using cameras for sensing. However, ‘cameras’ is a loaded term because we are typically familiar with an image sensor that produces RGB images and operates in the visible light spectrum. Depending on the application, this sensor could operate in infrared, which could be short wave, medium wave, long wave IR, or thermal imaging, to name a few variants. Event cameras, which are very hyper-sensitive to motion, were recently introduced. On an assembly line, line scan cameras are a slightly different variation from typical shutter-based cameras. Most current applications in automotive, surveillance, and medical rely on one or more of these sensors, which are often combined to do some form of sensor fusion to produce a result better than a single camera or sensor.”

Benefits
Generally speaking, MV can see better than people. The MV used in manufacturing can improve productivity and quality, lowering production costs. Paired with ADAS for autonomous driving, MV can take over some driving functions. Together with AI, MV can help analyze medical images.
The benefits of using machine vision include higher reliability and consistency, along with greater precision and accuracy (depending on camera resolution). And unlike humans, machines do not get tired, provided they receive routine maintenance. Vision system data can be stored locally or in the cloud, then analyzed in real-time when needed. Additionally, MV reduces production costs by detecting and screening out defective parts, and increases inventory control efficiency with OCR and bar-code reading, resulting in lower overall manufacturing costs.

Today, machine vision usually is deployed in combination with AI, which greatly enhances the power of data analysis. In modern factories, automation equipment, including robots, is combined with machine vision and AI to increase productivity.

How AI/ML and MV interact
With AI/ML, MV can self-learn and improve after capturing digital pixel data from sensors.
“Machine vision (MV) and artificial intelligence (AI) are closely related fields, and they often interact in various ways,” said Andy Nightingale, vice president of product marketing at Arteris IP. “Machine vision involves using cameras, sensors, and other devices to capture images or additional data, which is then processed and analyzed to extract useful information. Conversely, AI involves using algorithms and statistical models to recognize patterns and make predictions based on large amounts of data.”
This also can include deep learning techniques. “Deep learning is a subset of AI that involves training complex neural networks on large datasets to recognize patterns and make predictions,” Nightingale explained. ” Machine vision systems can use deep learning algorithms to improve their ability to detect and classify objects in images or videos. Another way that machine vision and AI interact is through the use of computer vision algorithms. Computer vision is a superset of machine vision that uses algorithms and techniques to extract information from images and videos. AI algorithms can analyze this information and predict what is happening in the scene. For example, a computer vision system might use AI algorithms to analyze traffic patterns and predict when a particular intersection will likely become congested. Machine vision and AI can also interact in the context of autonomous systems, such as self-driving cars or drones. In these applications, machine vision systems are used to capture and process data from sensors. In contrast, AI algorithms interpret this data and make decisions about navigating the environment.”

AI/ML, MV in autonomous driving
AI has an increasing number of roles in modern vehicles, but the two major roles are in perception and decision making.

“Perception is the process of understanding one’s surroundings through onboard and external sensor arrays,” said David Fritz, vice president of hybrid and virtual systems at Siemens Digital Industries Software. “Decision-making first takes the understanding of the surrounding state and a goal such as moving toward the destination. Next, the AI decides the safest, most effective way to get there by controlling the onboard actuators for steering, braking, accelerating, etc. These two critical roles address very different problems. From a camera or other sensor, the AI algorithms will use raw data from the sensors to perform object detection. Once an object is detected, the perception stack will classify the object, for example, whether the object is a car, a person, or an animal. The training process is lengthy and requires many training sets presenting objects from many different angles. After training, the AI network can be loaded into the digital twin or physical vehicle. Once objects are detected and classified decisions can be made by another trained AI network to control steering, braking, and acceleration. Using a high-fidelity digital twin to validate the process virtually has been shown to result in safer, more effective vehicles faster than simply using open road testing.”

How much AI/ML is needed is a question frequently asked by developers. In the case of modern factories, MV can be used to simply detect and pick out defective parts in an assembly line or employed to assemble automobiles. Doing the latter requires advanced intelligence and a more sophisticated design to ensure timing, precision, and calculation of motion and distance in the assembly process.
“Automation using robotics and machine vision has increased productivity in modern factories,” observed Geoff Tate, CEO of Flex Logix. “Many of these applications use AI. A simple application — for instance, detecting if a label is applied correctly — does not require a great deal of intelligence. On the other hand, a sophisticated, precision robot arm performing 3D motion requires much more GPU power. In the first application, one tile of AI IP will be sufficient, while the second application may need multiple tiles. Having flexible and scalable AI IPs would make designing robotics and machine vision much easier.”

Applications
Machine vision applications are limited only by one’s imagination. MV can be used in almost any industrial and commercial segment, so long as it requires vision and processing. Here is a partial list:
 Transportation (autonomous driving, in-cabin monitoring, traffic flow analysis, moving violation and accident detection);

•  Manufacturing and automation (productivity analysis, quality management);
•  Surveillance (detection of motion and intrusion monitor);
•  Health care (imaging, cancer and tumor detection, cell classification);
•  Agriculture (farm automation, plant disease and insect detection);
•  Retail (customer tracking, empty shelf detection, theft detection), and
•  Insurance (accident scene analysis from images).

There are many other applications. Consider drinking water or soft drink bottling. A machine vision system can be used to inspect fill levels, which typically is done by highly efficient robots. But robots occasionally make mistakes. MV can ensure the fill level is consistent and the labels are applied correctly.

Detecting any machine parts that deviate from measurement specification limits is another job for MV. Once the MV is trained on the specification, it can detect the parts that are outside the specification limits.

MV can detect uniform shapes such as squares or circles as well as odd-shaped parts, so it can be used to identify, detect, measure, count, and (with robots), pick and place.
Finally, combining AI, MV can perform tire assembly with precision and efficiency. Nowadays, OEMs automate vehicle assembly with robots. One of the processes is to install the four wheels to a new vehicle. Using MV, a robotic arm can detect the correct distance and apply just the right amount of pressure to prevent any damage.

Types of MV
MV technologies can be divided into one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D).

1D systems analyze data one line at a time, comparing variations among groups. Usually it is used in production of items such as plastics and paper on a continual basis. 2D systems, in contrast, use a camera to scan line by line to form an area or a 2D image. In some cases, the whole area is scanned and the object image can then be unwrapped for detailed inspection. 

3D systems consist of multiple cameras or laser sensors to capture the 3D view of an object. During the training process, the object or the cameras need to be moved to capture the entire product. Recent technology can produce accuracy within micrometers. 3D systems produce higher resolution but are also more expensive.

Emerging MV startups and new innovations
Tech giants, including IBM, Intel, Qualcomm, and NVIDIA, have publicly discussed investments in MV. In addition, many startups are developing new MV solutions such as Airobotics , Arcturus Networks, Deep Vision AI , Hawk-Eye Innovations, Instrumental, lending AI, kinara, Mech-Mind, Megvii, NAUTO, SenseTime, Tractable, ViSenze, Viso, and others. Some of these companies have been able to raise funding in excess of $1 billion.

In transportation, insurance companies can use MV to scan photographs and videos of scenes of accidents and disasters for financial damage analysis. Additionally, AI-based MV can power safety platforms to analyze driver behavior.

In software, computer vision platforms can be created without the knowledge of coding. Other startups have developed the idea for MV authentication software. And in the field of sports, AI, vision, and data analysis could provide coaches the ability to understand how decisions are made by players during a game. Also, one startup devised a cost reduction idea for surveillance by combining AI and MV in unmanned, aerial drone design.

Both MV and AI are changing quickly, and will continue to increase in performance, including precision and accuracy, while high GPU and ML power will come down in cost, propelling new MV applications.

Arteris’ Nightingale noted there will be further improvements in accuracy and speed. “Machine vision systems will likely become more accurate and faster. This will be achieved through advancements in hardware, such as sensors, cameras, and processors, as well as improvements in algorithms and machine learning models,” he said, pointing to an increased use of deep learning, as well. “Deep learning has been a significant driver of progress in machine vision technology in recent years, and it is likely to play an even more substantial role in the future. Deep learning algorithms can automatically learn data features and patterns, leading to better accuracy and performance. There will be an enhanced ability to process and analyze large amounts of data, as machine vision technology can process and analyze large amounts of data quickly and accurately. We may also see advancements in machine vision systems that can process significantly larger datasets, leading to more sophisticated and intelligent applications.”

Further, MV and AI are expected to integrate with other technologies to provide additional high-performance, real-time applications.

“Machine vision technology is already integrated with other technologies, such as robotics and automation,” he said. “This trend will likely continue, and we may see more machine vision applications in health care, transportation, and security. As well, there will be more real-time applications. Machine vision technology is already used for real-time applications, such as facial recognition and object tracking. In the future, we may see more applications that require real-time processing, such as self-driving cars and drones.”

MV design challenges
Still, there are challenges in training an MV system. Its accuracy and performance depend on how well the MV is trained. Inspection can encompass parameters such as orientation, variation of the surfaces, contamination, and accuracy tolerances such as diameter, thickness, and gaps. 3D systems can perform better than 1D or 2D systems when detecting cosmetic and service variation effects. In other cases, when seeing an unusual situation, human beings can draw on knowledge from a different discipline, while MV and AI may not have that ability.

“Some of today’s key challenges include data flow management and control – especially with real-time latency requirements such as those in automotive applications — while keeping bandwidth to a minimum,” said Alexander Zyazin, senior product manager in Arm‘s Automotive Line of Business. “In camera-based systems, image quality (IQ) remains critical. It requires a hardware design to support ultra-wide dynamic range and local tone mapping. But it also requires IQ tuning, where traditionally subjective evaluation by human experts was necessary, making the development process lengthy and costly. The new challenge for MV is that this expertise might not result in the best system performance, as perception engines might prefer to see images differently to humans and to one another, depending on the task.”

In general, machines can do a better job when doing mundane tasks over and over again, or when recognizing an image with more patterns than humans can typically process. “As an example, a machine may do a better job recognizing an anomaly in a medical scan than a human, simply because the doctor may make a mistake, be distracted or tired,” said Thomas Andersen, vice president for AI and machine learning at Synopsys. “When inspecting high-precision circuits, a machine can do a much better job analyzing millions of patterns and recognizing errors, a task a human could not do, simply due to the size of the problem. On the other hand, machines have not yet reached the human skill of recognizing the complex scenes that can occur while driving a car. It may seem easy for a human to recognize and anticipate certain reactions, while the machine may be better in ‘simple’ situations that a human easily could deal with, but did not due to a distraction, inattention or incapacitation – for example auto stop safety systems to avoid an imminent collision. A machine can always react faster than a human, assuming it interprets the situation correctly.”

Another challenge is making sure MV is secure. With cyberattacks increasing constantly, it will be important to ensure no production disruption or interference from threat actors.

“Security is critical to ensuring the output of MV technology isn’t compromised,” said Arm’s Zyazin. “Automotive applications are a good example of the importance of security in both hardware and software. For instance, the information processed and extracted from the machine is what dictates decisions such as braking or lane-keep assist, which can pose a risk to those inside the vehicle if done incorrectly.”

Conclusion
MV designs include a mixture of chips (processors, memories, security), IPs, modules, firmware, hardware and software. The rollout of chiplets and multi-chip packaging will allow those systems to be combined in novel ways more easily and more quickly, adding new features and functions and improving the overall efficiency and capabilities of these systems.

“Known good die (KGD) solutions can provide cost and space efficient alternatives to packaged products with limited bonding pads and wires,” said Tetsu Ho, DRAM manager at Winbond. That helps improve design efficiency, provides enhanced hardware security performance, and especially time-to-market for product launch. These die go through 100% burn-in and are tested to the same extent as discrete parts. KGD 2.0 is needed to assure end-of-line yield in 2.5D/3D assembly and 2.5D/3D multichip devices to realize improvements in PPA, which means bandwidth performance, power efficiency, and area as miniaturization, driven by the explosion of technologies such as edge-computing AI.”

This will open new options for MV in new an existing markets. It will be used to support humans in autonomous driving, help robots perform with precision and efficiency in manufacturing, and perform surveillance with unmanned drones. In addition, MV will be able to explore places that are considered dangerous for humans, and provide data input and analysis for many fields, including insurance, sports, transportation, defense, medicine, and more.

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[(July 5, 2023): There is a strong suspicion that this is a machine generated article and so its veracity is questionable.]

Experts Predict Stunning Growth for Global Image Sensor Market, Reaching USD 55.8 Billion by 2032

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A recent preprint titled "Practical Entropy Accumulation for Random Number Generators with Image Sensor-Based Quantum Noise Sources" by Choi et al. is available here:  https://www.preprints.org/manuscript/202306.1169/v1

 Abstract: The efficient generation of high-quality random numbers is essential in the operation of cryptographic modules. The quality of a random number generator is evaluated by the min-entropy of its entropy source. Typical method used to achieve high min-entropy of the output sequence is an entropy accumulation based on a hash function. This is grounded in the famous Leftover Hash Lemma which guarantees a lower bound on the min-entropy of the output sequence. However, the hash function based entropy accumulation has slow speed in general. For a practical perspective we need a new efficient entropy accumulation with the theoretical background for the min-entropy of the output sequence. In this work, we obtain the theoretical bound for the min-entropy of the output random sequence through the very efficient entropy accumulation using only bitwise XOR operations, where the input sequences from the entropy source are independent. Moreover we examine our theoretical results by applying to the quantum random number generator that uses dark noise arising from image sensor pixels as its entropy source.

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	RADOPT 2023: Workshop on Radiation Effects on      Optoelectronic Detectors and Photonics Technologies 28-30 Nov 2023 Toulouse (France)

First Call for Papers

You are cordially invited to participate to the second edition of the RADECS Workshop on Radiation Effects on Optoelectronics and Photonics Technologies (RADOPT 2023) to be held on 28^th-30^th November 2023 in Toulouse, France.

After the success of RADOPT 2021, this second edition of the workshop, will continue to combine and replace two well-known events from the Photonic Devices and IC’s community: the “Optical Fibers in Radiation Environments Days -FMR” and the Radiation effects on Optoelectronic Detectors Workshop, traditionally organized every-two years by the COMET OOE of CNES.

The objective of the workshop is to provide a forum for the presentation and discussion of recent developments regarding the use of optoelectronics and photonics technologies in radiation-rich environments The workshop also offers the opportunity to highlight future prospects in the fast-moving space, high energy physics, fusion and fission research fields and to enhance exchanges and collaborations between scientists. Participation of young researchers (PhD) is especially encouraged.
Oral and poster communications are solicited reporting on original research (both experimental and theoretical) in the following areas:

• Basic Mechanisms of radiation effects on optical properties of materials, devices and systems
• Silicon Photonics, Photonic Integrated Circuits
• Solar Cells
• Cameras, Image sensors and detectors
• Optically based dosimetry and beam monitoring techniques
• Fiber optics and fiber-based sensors
• Optoelectronics components and systems

Abstract Submission and Decision Notification:

Abstracts for both oral and poster presentations can be submitted. The final decision will be taken by the RADOPT Scientific Committee.

·       Abstract submission open: Monday April 3^rd, 2023

·       Abstract submission deadline: Friday July 9^th, 2023

à Send abstract to clementine.durnez@cnes.fr

Industrial Exhibition

An industrial exhibition will be organized during RADOPT 2023. The location allows the exhibits to be located adjacent to the auditorium where the oral sessions will be delivered. Please contact us for more details.

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Canon presentation on "PPA Optimization Using Cadence Cerebrus for CMOS Image Sensor Designs" is available here: https://vimeo.com/822031091

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The IEEE International Conference on Computational Photography (ICCP) program is now available online: https://iccp2023.iccp-conference.org/conference-program/

ICCP is an in-person conference to be held at the Monona Terrace Conventional Center in Madison, WI (USA) from July 28-30, 2023.

Early registration ends June 22: https://iccp2023.iccp-conference.org/registration/

Friday, July 28th

09:00 Opening Remarks

09:30 Session 1: Polarization and HDR Imaging
1) Learnable Polarization-multiplexed Modulation Imager for Depth from Defocus
2) Polarization Multi-Image Synthesis with Birefringent Metasurfaces
3) Glare Removal for Astronomical Images with High Local Dynamic Range
4) Polarimetric Imaging Spaceborne Calibration Using Zodiacal Light

10:30 Invited Talk: Melissa Skala (UW-Madison)
Unraveling Immune Cell Metabolism and Function at Single-cell Resolution

11:00 Coffee break

11:30 Keynote: Aki Roberge (NASA)
Towards Earth 2.0: Exoplanets and Future Space Telescopes

12:30 Lunch; Industry Consortium Mentorship Event

14:00 Invited Talk: Lei Li (Rice)
New Generation Photoacoustic Imaging: From Benchtop Wholebody Imagers to Wearable Sensors

14:30 Session 2: Emerging and Unconventional Computational Sensing
1) CoIR: Compressive Implicit Radar
2) Parallax-Driven Denoising of Passively-Scattered Thermal Imagery
3) Moiré vision: A signal processing technology beyond pixels using the Moiré coordinate

15:15 Poster and demo Spotlights

15:30 Coffee break

16:00 Poster and demo Session 1

17:30 Community Poster and Demo Session

Saturday, July 29th

09:00 Invited Talk: Ellen Zhong (Princeton)
Machine Learning for Determining Protein Structure and Dynamics from Cryo-EM Images

09:30 Session 3: Neural and Generative Methods in Imaging
1) Learn to Synthesize Photorealistic Dual-pixel Images from RGBD frames
2) Denoising Diffusion Probabilistic Model for Retinal Image Generation and Segmentation
3) NeReF: Neural Refractive Field for Fluid Surface Reconstruction and Rendering
4) Supervision by Denoising

10:30 Invited Talk: Karen Schloss (UW-Madison)

11:00 Coffee break

11:30 Keynote: Aaron Hertzmann (Adobe)
A Perceptual Theory of Perspective

12:30 Lunch; Affinity Group Meetings

14:00 Invited Talk: Na Ji (UC Berkeley)

14:30 Session 4: Measuring Spectrum and Reflectance
1) Spectral Sensitivity Estimation Without a Camera
2) A Compact BRDF Scanner with Multi-conjugate Optics
3) Measured Albedo in the Wild: Filling the Gap in Intrinsics Evaluation
4) Compact Self-adaptive Coding for Spectral Compressive Sensing

15:30 Industry Consortium Talk: Tomoo Mitsunaga (Sony)
Computational Image Sensing at Sony

16:00 Poster and Demo Spotlights

16:15 Coffee Break

16:45 Poster and Demo Session 2

18:15 Reception

Sunday, July 30th

09:00 Session 5: Depth and 3D Imaging
1) Near-light Photometric Stereo with Symmetric Lights
2) Aberration-Aware Depth-from-Focus
3) Count-Free Single-Photon 3D Imaging with Race Logic

09:45 Invited Talk: Jules Jaffe (Scripps & UCSD)
Welcome to the Underwater Micro World: The Art and Science of Underwater Microscopy

10:15 Coffee Break

10:45 Invited Talk: Hooman Mohseini (Northwestern University)
New Material and Devices for Imaging

11:15 Keynote: Eric Fossum (Dartmouth)
Quanta Image Sensors and Remaining Challenges

12:15 Lunch; Industry Consortium Mentorship Event

12:45 Lunch (served)

14:00 Session 6: NLOS Imaging and Imaging Through Scattering Media
1) Isolating Signals in Passive Non-Line-of-Sight Imaging using Spectral Content
2) Fast Non-line-of-sight Imaging with Non-planar Relay Surfaces
3) Neural Reconstruction through Scattering Media with Forward and Backward Losses

14:45 Invited Talk: Jasper Tan (Glass Imaging)
Towards the Next Generation of Smartphone Cameras

15:15 Session 7: Holography and Phase-based Imaging
1) Programmable Spectral Filter Arrays using Phase Spatial Light Modulators
2) Scattering-aware Holographic PIV with Physics-based Motion Priors
3) Stochastic Light Field Holography

16:00 Closing Remarks

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From JCN Newswire: https://www.jcnnewswire.com/english/pressrelease/82919/3/NEC-develops-the-world&aposs-first-highly-sensitive-uncooled-infrared-image-sensor-utilizing-carbon-

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Webinar on latest trends in High-Speed Camera, Introducing the BSI Camera Sensor.

Join this free tech talk by our expert speakers of Phantom High-Speed Cameras - Vision Research in which we explore the latest trends in high-speed cameras focusing on Backside Illuminated (BSI) sensor cameras and the associated benefits of improved processing speed and fill factor along with the challenges in such high-speed designs.

Webinar registration [link]

Date: 22nd June 2023
Time: 2:30pm IST / 2:00am Pacific / 5:00am Eastern

Topics to be covered:
Introducing the BSI sensor camera
Introducing FORZA & Sensor Insights
Introducing the MIRO C camera
Demo & display of the High-Speed Camera & its accessories.

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An interesting combination of tech+art: https://bjoernkarmann.dk/project/paragraphica 

Paragraphica is a context-to-image camera that uses location data and artificial intelligence to visualize a "photo" of a specific place and moment. The camera exists both as a physical prototype and a virtual camera that you can try.

Will this put the camera and image sensor industry out of business? :)

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By TechInsights Image Sensor Experts: Eric Rutulis, John Scott-Thomas, PhD

We first heard about it at IEDM 2021, and Sony provided more details at the 2022 IEEE Symposium on VLSI Technology and Circuits conference. Now it’s on the market and TechInsights has had our first look at the “world’s first” two-layer image sensor and we present our preliminary results here. The device was found in a Sony Xperia 1V smartphone main camera having a 48 MP, 1.12 µm pixel pitch and we can confirm it has dual photodiodes (a Left and Right photodiode in each pixel for full array PDAF). The die size measures 11.37 x 7.69 mm edge-to-edge.

In fact, the sensor actually has three layers of active silicon, with an Image Signal Processor (ISP) stacked in a conventional arrangement using a Direct Bond Interface (DBI) to the “second layer” (we will use Sony’s nomenclature when possible) of the CMOS Image Sensor (CIS). Figure 1 shows a SEM cross-section through the array. Light enters from the bottom of the image, through the microlenses and color filters. Each pixel is separated by an aperture grid (with compound layers) to increase the quantum efficiency. Front Deep Trench Isolation is used between each photodiode and it appears that Sony is using silicon dioxide in the deep trench to improve Full Well Capacity and Quantum Efficiency (this will be confirmed with further analysis).This layer also has the planar Transfer Gate used to transfer photocharge from the diode to the floating diffusion. Above the first layer is the “second layer” of silicon that contains three transistors for each pixel; the Reset, Amp (Source-Follower) and Select transistors. These transistors sit above the second layer silicon and connection to the first layer is achieved using “Deep contacts” which pass through the second layer essentially forming Through Silicon Vias (TSVs). Finally, the ISP sits above the metallization of the second layer, connected using Hybrid (Direct) Bonding. The copper of the ISP used for connection to the CIS DBI Cu is not visible in this image.

Key to this structure is a process flow that can withstand the thermal cycling needed to create the thermal oxide and activate the implants on the second layer. Sony has described the process flow in some detail (IEDM 2021, “3D Sequential Process Integration for CMOS Image Sensor”).

Figure 2 is an image from this paper showing the process flow. The first layer photodiodes and Transfer Gate are formed, and the second layer is wafer bonded and thinned. Only then are the second layer gate oxides formed and the implants are activated. Finally, the deep contacts are formed, etching through the second layer, and contacting the first layer devices.

Figure 2: Process flow for two-layer CIS described in “3D Sequential Process Integration for CMOS Image Sensor”, IEDM 2021.

The interface between the first and second layer is shown in more detail in Figure 3. The Transfer Gate (TG in the image) is connected to the first metal layer of the second layer. Slightly longer deep contacts lie below the sample surface and are partially visible in the image. These connect the floating diffusion node between the first and second layer. A sublocal connection (below the sample surface) is used to interconnect four photodiodes just above the first layer to the source of the Reset FET and gate of the AMP (Source-Follower) FET.

                    Figure 3: SEM cross-section detail of the first and second layer interface.

The sublocal connection is explored more in Figure 4. This is a planar SEM image of the first layer at the substrate level. Yellow boxes outline the pixel, with PDL and PDR indicating the left and right photodiodes. One microlense covers each pixel. Sublocal connections are indicated and are used to interconnect the Floating Diffusion for two pixels and ground for four pixels. The sublocal connection appears to be polysilicon; this is currently being confirmed with further analysis.

Figure 4: SEM planar view of the pixel first layer at the substrate level.

The motivation for the two-layer structure is multiple. The photodiode full well capacity can be maintained even with the reduced pixel pitch. The use of sublocal contacts reduces the capacitance of the floating diffusion, increasing the conversion gain of the pixels. The increased area available on the second layer allows the AMP (Source-Follower) transistor area to be increased, reducing noise (flicker and telegraph) create in the channel of this device.

It's worth taking a moment to appreciate Sony’s achievement here. The new process flow and deep contact technology allow two layers of active devices to be interconnected with an impressive 0.46 µm (center-to-center) spacing of the deep contacts (or Through Silicon Vias). Even the hybrid bonding to the ISP is just 1.12 µm; the smallest pitch TechInsights has seen to date. At the recent International Image Sensors Workshop, Sony described an upcoming generation that will use “buried” sublocal connections embedded in the first layer and pixel FinFets in the second layer (to be published). Perhaps we are seeing the first stages of truly three-dimensional circuitry, with active devices on multiple layers of silicon, all interconnected. Congratulations, Sony!

TechInsights' first Device Essentials analysis on this device will be published shortly with more analyses underway.

Access the TechInsights Platform for more content and reports on image sensors.

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