Samsung to Develop a 576-Megapixel Smartphone Sensor by 2025

Hot on the heels of its recently-launched 200-megapixel ISOCELL HP1 smartphone sensor, Samsung has announced that it plans to develop a 576-megapixel smartphone sensor by 2025.

Announced during a Samsung presentation at the SEMI Europe Summit and spotted by Image Sensors World, the company made it known that it plans to be able to scale down pixels -- as it has been doing progressively since the year 2000 -- to such a degree that a 576-megapixel smartphone sensor would be possible in just four years.

As shown in the slide below, Samsung has been progressively scaling down the size of its pixels and additionally increasing their megapixel counts consistently over the last two decades, most notably since 2010. It was able to scale from 5-megapixels up to 16 megapixels in four years, then from 16-megapixels to 64-megapixels in four more years. In 2020 it created a 108-megapixel sensor and just last week announced a 200-megapixel sensor. While a 576-megapixel sensor sounds extraordinary, the company's progression to this point seems to indicate that the timeline should be more than feasible if the technology to continue to reduce the size of pixels continues to advance.

As noted by DPReview, Samsung announced that it planned to push beyond 500-megapixel sensors in April of 2020 which shows that the company has had its goals set on such resolution in smartphones for some time. The company seems to indicate that 600-megapixels is its current target for resolution in an effort to mimic what it believes to be equivalent to -- or better than -- the human eye.

"The image sensors we ourselves perceive the world through – our eyes – are said to match a resolution of around 500 megapixels (Mp). Compared to most DSLR cameras today that offer 40Mp resolution and flagship smartphones with 12Mp, we as an industry still have a long way to go to be able to match human perception capabilities," Samsung says. "Through relentless innovation, we are determined to open up endless possibilities in pixel technologies that might even deliver image sensors that can capture more detail than the human eye."

Samsung notes that it understands that smaller pixels can result in "fuzzy" or "dull" photos, and that part of the task of its engineers is not only to continue to make pixels smaller but balance that with image quality. How the company plans to do this is not revealed, but if the image quality of its new HP1 sensor stands up to scrutiny -- which will require Samsung releases image samples, which it has not yet done -- there is no reason to believe the company can't achieve these goals.

Image credits: Header photo licensed via Depositphotos.

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Sony Leads Global Smartphone Sensor Production, Samsung Closes Gap

The global smartphone sensor market was not slowed down by the pandemic, as a new report states the segment saw a total revenue of $15 billion in 2020 which is up 13% year over year. Sony still dominates the field, but its once ironclad grip has slipped slightly.

Sony used to control over 50% of total smartphone sensor production with Samsung in a distant second with less than 20% market share according to a report published last year. But as noted by DIY Photography, new data from Strategy Analytics -- published by EET Asia -- shows that has changed, as Samsung jumped to 29% market share and Sony fell to 46%.

While Sony still makes outstanding sensors, Samsung has seriously stepped its game up. Not only does its sensor perform better than any of its previous endeavors in the Galaxy S21 Ultra, but the company has also done a great job marketing the new technology it is producing and explaining how it works. Perhaps more importantly, Samsung was able to fulfill orders that Sony passed on, allowing it to increase its production and overall market share.

In November of 2020, Nikkei Asia published a report that stated Sony was in an unfortunate position of backing a losing horse and as a result, was losing market share to Samsung. Because Sony was expecting Huawei to produce significantly more phones than they ended up being able to produce, other manufacturers were turned away with the expectation that Sony would not be able to meet any additional orders.

When Huawei was added to the economic blacklist in the United States, that changed. In one year, Huawei went from commanding 41% of the Chinese smartphone market at the beginning of 2020 to just 16% at the beginning of 2021. Unable to source parts and blocked from using the latest versions of Google's Android operating system and without access to the Google Play Store, Huawei is floundering and its future looks bleak.

Vivo X60 Pro+ | Photo by Ted Kritsonis

Taking its place are the three big brands owned by BBK electronics: Oppo, Vivo, and OnePlus. While OnePlus launched its OnePlus 9 and Pro phones with custom Sony sensors as did Oppo with its Find X3, Vivo's X60 devices feature a Samsung GN1 sensor. Theoretically, if the collapse of Huawei had been predicted, Sony would have been able to supply all three brands with Sony sensor tech. Losing just one may not be a big deal as Vivo isn't even the strongest brand of the three, but it is a sign that competition is heating up.

It should be noted that while all three new smartphone lines perform admirably, the Vivo X60 Pro+ might be the device with the best camera system of the batch. While this isn't necessarily because of the Samsung sensor on board, it certainly doesn't hurt the company's growing reputation for quality components.

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Samsung to Use Neural Network to Kill ‘Bad’ Pixels, Improve Image Quality

CMOS image sensors, while amazing in many ways, aren't flawless: they are affected by many different types of noise introduction that can reduce image quality. That noise can lead to corrupted pixels -- or "bad" pixels -- and Samsung has unveiled a new method to get rid of them: a neural network.

Originally presented at the International Conference on Computer Vision and Image Processing, this paper by Samsung Electronics' Girish Kalyanasundaram was recently published online and noticed by [_Image Sensors World >. In it, the Kalyanasundaram explains how Samsung is investigating combating "bad" pixels and sensor noise by using a pre-processing assisted neural network.

"The proposed method uses a simple neural network approach to detect such bad pixels on a Bayer sensor image so that it can be corrected and overall image quality can be improved," the abstract reads. "The results show that we are able to achieve a defect miss rate of less than 0.045% with the proposed method."

As the resolution of Bayer sensors increases, specifically sensors as small as those found in smartphones, they are becoming more susceptible to various types of unwanted noise. Any of the various types of noise can lead to a distortion of a pixel's intensity and therefore a deterioration of perceived image quality.

"Such pixels are called ‘bad pixels’, and they can be of two types: static and dynamic," Kalyanasundaram explains. "Static pixels are those with permanent defects, which are introduced during the manufacturing stage and are always fixed in terms of location and intensity. These kinds of pixels are tested and their locations are stored in advance in the sensor’s memory so they can be corrected by the image sensor pipeline (ISP). Dynamic bad pixels are not consistent. They change spatially and temporally, which makes them harder to detect and correct."

(a) Region of an image. (b) Region of the same image but with simulated bad pixels in the Bayer image before demosaicing.

Because these dynamic pixels are constantly in flux, Samsung believes the best way to combat them is with a system that is capable of recognizing those bad pixels as they appear and proactively removing them. The researchers tried two different neural network architectures to see if the concept would work, and found that both methods performed much better in detection accuracy than the reference method, although one resulted in a higher number of false positives although it registered lesser misses compared to the other. In the reference image below, "NN" is short for Neural Network.

Illustration of bad pixels (in 300% zoom), a heat map of Misses and False Positives for NN II with a = 16. (a) The demosaiced version of a portion of test resolution chart with the simulated bad pixels. The red circles show which pixels are being missed by the network. (b) Heat map of the false positives. The color code of the pixels shows the pixel channels (R,G or B) that are falsely detected. (c) Heat map of the misses. The red circles show which pixels are being missed by the network.

"The examples of misses circled in red show that the neural networks still have problems identifying some significantly bad cases, which indicates a scope for improvement, upon which current work is going on," Kalyanasundaram writes. "Since the false positives occur around edge regions, the use of a good quality correction method can ensure that the misdetection of these pixels as bad pixels will not have any deteriorating effects after correction, especially around edge regions."

While the details are highly technical, the concept is simple: use a neural network to predict, find, and eliminate bad pixels in image capture to allow for better overall image quality despite the high resolution that is being crammed onto the small sensors found on smartphones. It remains to be seen if this method can be rolled out to consumer devices, but on paper, the research sounds compelling.

Image credits: Header image uses assets licensed via Deposit Photos.

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