Archives for November 2021

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iNews, Lieyunwang, iFeng: Guangzhou Tyrafos (Chinese name Guangzhou Yinxin Semiconductor Technology Co., Ltd.) announces the A+ round of financing led by Yunqi Capital. By the end of this round of financing, Yinxin Semiconductor is expected to receive hundreds of millions of yuan in the near future.

Tyrafos also announces announced the release of "the world's first non-single photon dToF" (possibly, they mean dToF that is not based on SPADs) and ELISA molecular biological detection solution basedon image sensors.

"The world's first non-single-photon dToF released by Yinxin Semiconductor is the world's first technical solution to achieve direct ToF using CMOS process. Not only the accuracy and resolution are several times higher than the existing SPAD (Single Photon Avalanche Diode) solution. In addition, the overall power consumption of the laser light source at the transmitting end and the sensor at the receiving end has been reduced by 90%, catching up with the core technology of foreign manufacturers such as Apple and STMicroelectronics in this field, and the underlying architecture and circuit IP are independently developed by Yinxin Semiconductor, it solves the technical problem of the neck stuck in the field of 3D perception, and is a milestone breakthrough in the field of 3D perception."

"In the field of fingerprints under the optical screen, Yinxin Semiconductor adopts the innovative design of pixel architecture level to break through the area and cost limits. The chip area is 30%~50% smaller than that of competing products, and the module volume is more than half smaller than that of competing products. It has an ultra-high cost performance. Competitive advantage, in 2019, through cooperation with customers, it became the first optical under-screen fingerprint recognition solution certified by South Korea's Samsung mobile phone. At present, Yinxin Semiconductor has reached an agreement with 4-5 global leading fingerprint solution providers to purchase under-screen fingerprint chips. Intentional orders are expected to contribute hundreds of millions of yuan in revenue."

"The world's first digital ELISA molecular biological detection solution released by Yinxin Semiconductor is the world's first digital solution that directly performs molecular biological detection on the surface of an image sensor. Enzyme Linked Immunosorbent Assay (Enzyme Linked Immunosorbent Assay, abbreviated ELISA or ELASA) refers to a qualitative and quantitative detection method that combines soluble antigens or antibodies on solid-phase carriers such as polystyrene, and uses antigen-antibody specific binding for immunoreaction. . Taking advantage of the company's advantages in high-speed and high-sensitivity image sensors, Yinxin Semiconductor has created a complete set of micro-molecular biological detection equipment based on ELISA standards, achieving a zero breakthrough in this field, and surpassing the United States in detection accuracy and speed. NASDAQ listed company Quanterix (the core technology comes from the Harvard University technical team, the technology founder David Walt is an academician of the American Academy of Engineering and the Medical School, and is also the scientific founder of Illumina) SiMoA (Single-molecule Array) system, and Yinxin Semiconductor In terms of equipment cost and consumables cost, the digital ELISA technology of the company has a significant improvement over Quanterix's SiMoA system technology. The sensitivity of the digital ELISA technology is more than 1000 times higher than that of the traditional ELISA. Its appearance brings protein detection technology directly into the era of single-molecule and digital detection, becoming the true king in the field of fg-level ultra-low abundance protein detection. The optimization of detection accuracy, speed and cost is more conducive to application promotion. In the future, it will have great application potential in the fields of coronavirus detection, influenza virus detection, and early cancer screening."

Tyrafos has been established in May 2019 and its core team coming from TI, Omnivision, Samsung, Himax, TSMC, Mediatek, ASE, Foxconn, and Chi Mei. The company has >90 full-time R&D and operations employees. The core members of the team have 15-20 years experience in the semiconductor industry. The company names Sony and Samsung as its strategic partners, and TSMC, Samsung, and Tower as its foundries.

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 Lucid Vision Labs publish an explanatory video about Sony IMX490 5.4MP automotive sensor:

Lucid Vision Labs also publishes a measured EMVA1288 performance of the sensor:

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Verve.vc publishes an interview with Serguei Okhonin, Actlight CEO and co-founder. Few quotes:

Q: So how does an IP company such as ActLight actually work?

A: We started with an idea and developed it up to the point where we could file patents. The goal, of course, is to license the technology to big semiconductor companies that integrate it into their offering, which they can eventually sell to the big consumer brands we all know. What we do is research and refine our technology to the point that we can provide potential customers with prototypes that should, ideally, convince them of the merits of our sensor. When we achieve this, our job is done. We then basically just provide support.

Q: Let me guess: Convincing big semiconductor companies isn’t that easy.

A: Our potential customers are global market leaders, they have tens of thousands of employees and decades of experience in their field. Now imagine that a small startup from Lausanne comes up to them and says: Look guys, our detector performs better than what you have developed. The first thing they tell you is: That’s impossible. It takes many meetings and a lot of time to convince them otherwise. And then, they do a very thorough due diligence. They will spend more than a year to ascertain the merits of new technology, it is a long process. But once they have done that, and decide to move forward, these companies move very quickly. They have the manpower, the infrastructure, the sales organization, the relationships with their customers… they can bring a product to the mass market at a speed that no startup can match.

Q: Where do you stand in the process you just described?

A: For an IP company like ActLight, the most important achievement is what I call the industrial proof of technology, which means, that a mass-market product incorporates the technology we have developed. We have achieved this milestone now. We licensed our technology to a semiconductor company, the number 1 worldwide, which is building a heart-rate monitor for hearable devices. Thanks to our invention, power consumption is drastically reduced and a high level of miniaturization is achieved. Working samples are already at end customers and, in 2022, mass production starts.

Q: With a use case that will hit the market soon and others that sound promising, ActLight must have appeared on the radar of tech companies by now.

A: After 10 years, we need to think about the future of the company, and there are basically two options. Do we want to grow and pursue more projects, or do we want to sell the company? Recently, we have started to get approached by potential buyers. Of course, such approaches out of the blue need to be examined very well, but it doesn’t surprise me that this happens now.

Q: Because of the traction or what you called industrial proof of technology?

A: Not only that, the M&A activity in the semiconductor industry is incredibly intense at the moment. The sector is red hot. Hardware stocks have reached new highs, and the shortage of semiconductors is talked about a lot. There is an urgency to invest that I haven’t seen in the past 3 decades, it’s insane.

TheBusinessTime publishes an article about Actlight in-ear vital signs monitoring solution.

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• the latest analysis and trends of CMOS image sensors - resolution, pixel pitch, chip stacking and die configuration
• active silicon thickness and pixel aspect ratio trends
• trends and comparative analysis on ToF sensors, including both Front- and Back-illuminated, as well as recent NIR-optimized sensors
• CIS trends for emerging applications and future challenges

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TheElec: Samsung is to use chip-scale packaging (CSP) to reduce the cost of its low resolution image sensors starting 2022, according to TheElec sources. Currently, Samsung uses chip on board (COB) approach for all sensors.

CSP is done at the wafer level, unlike COB, resulting in increased productivity and lower assembly clean room requirements. 

The downside of CSP can only be done in lower resolution image sensors. Most higher resolution image sensors are manufactured with COB. TheElec sources say that CSP can support up to 2MP resolution at as of now.

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Counterpoint Research reports that CIS content per smartphone will expand to an average of 4.1. Despite the global components crunch, CIS growth is expected to grow by double digits to reach almost 6bn units in 2021.

“A big driver has been triple-and-above main camera setups, which accounted for two-thirds of all smartphones sold during the first half,” notes Tarun Pathak, Counterpoint’s director of smartphone research. “What’s really interesting is where a lot of that growth is coming from – Africa, Latin America, India and other emerging markets. As we move through post-COVID upgrade cycles, especially in Android heavy markets, we’re seeing OEMs offer increasingly sophisticated camera hardware to their customers across all segments.”

“High-resolution has also been an area of focus, with 48MP-plus becoming standard. Again, we’re seeing emerging markets lead in growth; and 64MP is starting to become a major segment too. High-res is very important for what is the most hotly contested price band globally – the wholesale $100-$399 category. During the second quarter, two-thirds of devices were high-res and we expect further share increases for the full year.”

“If you’re a product manager today delivering a quad cam device, then you’re probably thinking of configuring wide + ultrawide + macro + depth. But the playing field changes quickly, and we’re likely to see macro and ultrawide merge, leaving room for even more options like telephoto or time-of-flight. Increasing choice and complexity is why algorithm development has become such a critical factor in the success of camera systems,” states Ethan Qi, Counterpoint’s lead camera components analyst.

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TalkAndroid, GSMArena: Mediatek presents a 4nm Dimensity 9000 application processor for future smartphones with quite impressive imaging features:

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National University of Singapore and Korea Yonsei University present an ACM paper "LAPD: Hidden Spy Camera Detection using Smartphone Time-of-Flight Sensors" by Sriram Sami, Sean Rui Xiang Tan, Bangjie Sun, and Jun Han and a poster "On Utilizing Smartphone Time-of-Flight Sensors to Detect Hidden Spy Cameras" by the same authors. The detection was tested on  Samsung Galaxy S20+, S20 Ultra 5G, and Note 10+ containing VGA Sony IMX516 iToF sensor. Although the sensor has VGA resolution, the current Android API only provides a 240 × 320 image.

"Tiny hidden spy cameras concealed in sensitive locations including hotels and bathrooms are becoming a significant threat worldwide. These hidden cameras are easily purchasable and are extremely difficult to find with the naked eye due to their small form factor. The state-of-the-art solutions that aim to detect these cameras are limited as they require specialized equipment and yield low detection rates. Recent academic works propose to analyze the wireless traffic that hidden cameras generate. These proposals, however, are also limited because they assume wireless video streaming, while only being able to detect the presence of the hidden cameras, and not their locations. To overcome these limitations, we present LAPD, a novel hidden camera detection and localization system that leverages the time-of-flight (ToF) sensor on commodity smartphones. We implement LAPD as a smartphone app that emits laser signals from the ToF sensor, and use computer vision and machine learning techniques to locate the unique reflections from hidden cameras. We evaluate LAPD through comprehensive real-world experiments by recruiting 379 participants and observe that LAPD achieves an 88.9% hidden camera detection rate, while using just the naked eye yields only a 46.0% hidden camera detection rate."

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National University of Singapore and Korea Yonsei University present an ACM paper "LAPD: Hidden Spy Camera Detection using Smartphone Time-of-Flight Sensors" by Sriram Sami, Sean Rui Xiang Tan, Bangjie Sun, and Jun Han and a poster "On Utilizing Smartphone Time-of-Flight Sensors to Detect Hidden Spy Cameras" by the same authors. The detection was tested on  Samsung Galaxy S20+, S20 Ultra 5G, and Note 10+ containing VGA Sony IMX516 ToF sensor. Although the sensor has VGA resolution, the current Android API only provides a 240 × 320 image.

"Tiny hidden spy cameras concealed in sensitive locations including hotels and bathrooms are becoming a significant threat worldwide. These hidden cameras are easily purchasable and are extremely difficult to find with the naked eye due to their small form factor. The state-of-the-art solutions that aim to detect these cameras are limited as they require specialized equipment and yield low detection rates. Recent academic works propose to analyze the wireless traffic that hidden cameras generate. These proposals, however, are also limited because they assume wireless video streaming, while only being able to detect the presence of the hidden cameras, and not their locations. To overcome these limitations, we present LAPD, a novel hidden camera detection and localization system that leverages the time-of-flight (ToF) sensor on commodity smartphones. We implement LAPD as a smartphone app that emits laser signals from the ToF sensor, and use computer vision and machine learning techniques to locate the unique reflections from hidden cameras. We evaluate LAPD through comprehensive real-world experiments by recruiting 379 participants and observe that LAPD achieves an 88.9% hidden camera detection rate, while using just the naked eye yields only a 46.0% hidden camera detection rate."

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National University of Singapore and Korea Yonsei University present an ACM paper "LAPD: Hidden Spy Camera Detection using Smartphone Time-of-Flight Sensors" by Sriram Sami, Sean Rui Xiang Tan, Bangjie Sun, and Jun Han and a poster "On Utilizing Smartphone Time-of-Flight Sensors to Detect Hidden Spy Cameras" by the same authors. The detection was tested on  Samsung Galaxy S20+, S20 Ultra 5G, and Note 10+ containing VGA Sony IMX516 ToF sensor. Although the sensor has VGA resolution, the current Android API only provides a 240 × 320 image.

"Tiny hidden spy cameras concealed in sensitive locations including hotels and bathrooms are becoming a significant threat worldwide. These hidden cameras are easily purchasable and are extremely difficult to find with the naked eye due to their small form factor. The state-of-the-art solutions that aim to detect these cameras are limited as they require specialized equipment and yield low detection rates. Recent academic works propose to analyze the wireless traffic that hidden cameras generate. These proposals, however, are also limited because they assume wireless video streaming, while only being able to detect the presence of the hidden cameras, and not their locations. To overcome these limitations, we present LAPD, a novel hidden camera detection and localization system that leverages the time-of-flight (ToF) sensor on commodity smartphones. We implement LAPD as a smartphone app that emits laser signals from the ToF sensor, and use computer vision and machine learning techniques to locate the unique reflections from hidden cameras. We evaluate LAPD through comprehensive real-world experiments by recruiting 379 participants and observe that LAPD achieves an 88.9% hidden camera detection rate, while using just the naked eye yields only a 46.0% hidden camera detection rate."

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Université Grenoble Alpes publishes Maxence Bouvie's PhD thesis "Study and design of an energy efficient perception module combining event-based image sensors and spiking neural network with 3D integration technologies."

"Event-driven acquisition permits to generate sparse data, with high acquisition speed at the order of the microsecond, while conserving an exceptionally large dynamic range. Event-driven imagers are thus highly suited for deployment in situations where speed and application robustness are of high importance. However, event-based image sensors come with major drawbacks that render them nearly impracticable in embedded situations. They are noisy, poorly resolved and generate an incredible amount of data relatively to their resolution. This Ph.D. study thus focuses on understanding how they can be used, and how their drawbacks can be alleviated. The work explores bio-inspired applications for tasks where frame-based methods are already successful but present robustness flaws because classical frame-based imagers cannot be intrinsically high speed and high dynamic range. This manuscript provides leads to understand and decide why some algorithms matches more than other to their novel data type. It also tries to touch upon the reasons these sensors cannot be used as they are, but how they could be efficiently integrated into classical frame-based algorithmic pipelines and systems by deploying motion compensation of the raw data. In addition, a bio-inspired hardware-based solution to simultaneously reduce the output bandwidth and filter out noise, directly at the output of a grid of event-based pixels, is presented. It consists in the hardware implementation of a bio-inspired convolutional neural network accelerator - a neuromorphic processor – distributed near-sensor, that takes major advantages from being conceived toward a three-dimensional integration. This system was designed for minimizing its power budget, at the 28nm FDSOI node, and demonstrates a 2.86pJ per synaptic operation – or 93.0aJ per input event per pixel. On top of that, it is scalable for megapixel resolution sensors without induced overhead."

Appendix C (pp. 134-135) gives a nice comparison of Samsung, Prophesee (Sony), and Celepixel (Omnivision) event-driven sensor approaches.

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