Previously, CIS products relied on Frontside Illumination (FSI) technology which was vulnerable to diffraction and impacted the image quality. However, the introduction of BSI proved to be a transformative development for CIS technology as it resolved the issues of FSI and led to the development of next-generation CIS products.

In this EE Times article, Technical Leader Kyoung-in Lee from SK hynix explains in detail how developments in BSI technology led to significant upgrades in image sensors. These developments include smaller pixel sizes, high-resolution products with millions of pixels, the prevention of light diffraction, and an increase in the quantum efficiency (QE) of products—which makes it possible to display bright images even in low-light conditions.

SK hynix is continuously evolving its BSI technology. The company first introduced basic element technologies including Backside Deep Trench Isolation (BDTI) and Air Grid—which increases the QE of products. More recently, the company succeeded in developing hybrid bonding technology that applies ‘Cu-to-Cu bonding’ to stacked sensors based on TSV (Through Silicon Via), laying the foundation for increased competitiveness in chip size and expansion of multilayer wafer bonding technology.

Furthermore, these technological achievements are expected to contribute to the expansion of the market by being utilized in the development of various sensors that support AI, medical devices, AR, and VR in the future.

To learn more about BSI technology, check out the full article on EE Times: SK hynix’s BSI Technology a Leading Light in the Global Mobile Market

The post SK hynix’s BSI Technology a Leading Light in the Global Mobile Market first appeared on SK hynix Newsroom.

Cameras are one of the media which record objects embodied by light, allowing people to express various emotions, identity, and philosophy either objectively or subjectively. In particular, we are in a so-called “digital nomad era” where people carry digital devices and live without being restricted from time and space. In this era, digital cameras equipped with image sensors are more widely used than film cameras. It is also the era of smartphones, which are equipped with digital camera functionality. In digital cameras or smartphone cameras that record our daily life and memories, the role of image sensors is like films in film cameras. Image sensors play a key role in converting the information of the subject received through the lens into an electrical image signal.

Figure 1. Image sensor applications & Image generation mechanism

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Image sensors can be largely divided into CCD image sensors and CMOS image sensors based on their applications and manufacturing processes. In particular, CMOS image sensors (CIS) are going beyond digital cameras and are actively applied to various new market fields, such as smartphones, tablets, CCTVs, black boxes for cars, autonomous vehicle sensors, virtual reality (VR), medical equipment, and drones. Through this, sales of this semiconductor product line are showing rapid growth¹.

The operation process of a CMOS-based image sensor is as follows: When the light energy in the visible light wavelength range (400 to 700nm) is condensed on the photodiode (PD) of the silicon substrate, the silicon surface receives the light energy to form an electron-hole pair. The electron generated in this process is converted into voltage through floating diffusion (FD) and then converted into digital data through an analog to digital converter (ADC). To make such a CIS product making the series of these processes possible, key manufacturing process technologies unique to CIS, which are different from the ones for semiconductor memory, are required. These process technologies can be classified into five categories.

1. Deep PD Formation Process Technology

Consumers’ desire for clearer image quality led to competition to increase the pixel density and resolution in mobile CIS, accelerating the development of CIS process technology. The pixel size should be more reduced to increase the number of pixels in the same chip size. Also, forming deep PD is a key technology to avoid deterioration in image quality. To secure sufficient full well capacity (FWC) in small pixels, patterning and implementing technologies with higher difficulty level compared to the ones for semiconductor memory are required. Especially, it is essential to secure a high aspect ratio (>15:1) implant MASK process technology that can block high-energy ion implantation; in fact, the aspect ratio tends to be gradually increasing in the industry these days².

Figure 2. Schematic diagram of photo diode structure change along with reduction in pixel size

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2. Pixel to Pixel Isolation Process Technology

The technology to isolate pixels from one another is very important to make a high-definition CIS. A less developed isolation technology can cause various image defects such as color mixing and color spreading. Each chipmaker has different isolation technology, and the difference will be an important criterion for image quality in the CIS market where higher pixel density and higher resolution are becoming common standards. Various issues can occur during the isolation process. For this reason, huge efforts are being made to select better equipment and develop new recipes to improve yield and product quality.

3. Color Filter Array (CFA) Process Technology

Color filter array (CFA) is a process unique to CIS that is not seen in the semiconductor memory process. The CFA process is generally composed of a color filter (CF) that filters the incident light into red, green, and blue for each wavelength range, and a microlens (ML) to increase efficiency in condensing. To create excellent image quality, it is important to develop and evaluate R/G/B color materials and develop technologies to optimize process conditions such as shape and thickness. Recently, a series of high-quality and high-functional CIS products are being released, thanks to the development of various application technologies such as Bayer and Quad being combined with the basic form of CFA.

Figure 3. Structure of CFA (Color Filter Array)

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4. Wafer Stacking Process Technology

Wafer stacking refers to attaching two wafers together. This is an essential technology for making high-pixel and high-definition CIS products³. For high-pixel CIS products, pixel arrays and logic circuits are formed on individual wafers separately. These wafers are attached together in the middle of the process, which is called “wafer bonding”. Separation of the pixel arrays and logic circuits would increase manufacturing cost, but more chips can be produced in the same amount of wafer area, and it also helps improve product properties. As a result, it is a technology adopted by most CIS chipmakers these days. Wafer stacking technology is continuously developing in various forms. Recently, the wafer stacking technology has been applied to the semiconductor memory segment as well, contributing to the improvement of the product properties.

Figure 4. Wafer Stacking configuration

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5. Control Technology to Improve CIS Product Yield and Quality

One of the most fundamental requirements in the CIS product development and mass production process is to control metallic contamination. Since CIS products are sensitive to contamination several times more than memory products and the contamination directly affects product yield and quality, various contamination control technologies are required. The next important factor is the plasma damage^{4 5} control. Since the deterioration of image properties such as hot pixels occurs due to the damage caused during the process, it is necessary to manage key processes accurately.

This article covered the main characteristics of the key CIS process technologies. It is no exaggeration to say that the completeness of CIS products is determined by not only the process technology, but also how organically pixel devices, analog and digital design, and image signal processing (ISP) technology are complementing each other and being optimized. Based on the existing semiconductor memory process technologies, SK hynix has secured the core process technologies which are unique to CIS products, including the ones listed above. Through this, the Company is proactively responding to the market demand, with the in-time development of high-pixel and high-definition products. In the future, based on its process technologies, devices, design, and ISP technology, the CIS business of SK hynix is expected to expand its base to various application product lines in different fields, such as medical and security, as well as mobile CIS products. Ultimately, it is expected that this will contribute to the creation of both economic and social values that SK Group pursues.

¹Yole Development, “Status of the CMOS image sensors industry 2012”, https://www.slideshare.net/Yole_Developpement/yole-cmos imagesensorsoctober2012reportsample
²SONY.net, “Perspectives from the creators of the image sensor ‘microcosm’” https://www.sony.net/SonyInfo/technology/stories/IMX586/
³Cheng-Ta Ko, et al., “Wafer-to-wafer hybrid bonding technology for 3D IC” 3RD Electronics System Integration Technology Conference ESTC, Berlin, 2010, pp. 1-5, doi: 10.1109/ESTC.2010.5642848.
⁴Koji Eriguchi, “Defect generation in electronic devices under plasma exposure: Plasma-induced damage” Jpn. J. Appl. Phys. 56 (2017)
⁵K. Eriguchi., “Application of Molecular Dynamics Simulations to Plasma Etch Damage in Advanced Metal-Oxide-Semiconductor Field-Effect Transistors”, Molecular Dynamics− Studies of Synthetic and Biological Macromolecules, 221-244 (2012)

The post Applying Light to Semiconductors: Introducing to CIS Key Process Technologies first appeared on SK hynix Newsroom.

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Visual Evolution – from the Cambrian Era to Today

Close your eyes and imagine a world without eyesight.

Millions of years ago, that was the way of life on Earth. It was nearly 540 million years ago that animals first developed the ability to see – transforming everything about the ways in which they could avoid enemies, secure food, and evolve into various species.¹ It led to a geological event known as the Cambrian explosion, where the number of animal groups exploded from three to 38.

A similar visual innovation took place much more recently with the proliferation of smartphones in the late 2000s. Suddenly, people around the world were equipped with high-performance cameras that could fit into their pocket or the palm of their hand. Photography was no longer restricted to photographers and the easy transfer of visual information became widely available.

This year, with the outbreak of COVID-19, the transition to the contactless digital era accelerated once again as video conferencing and online classes became a part of our daily lives.

¹Andrew Parker, “In the Blink of an Eye: How Vision Sparked the Big Bang of Evolution”, (2003) (URL)

“Retina of Camera” CIS Technology Development, the Heyday of Smartphone Cameras

Figure 1. Structure of CMOS Image Sensor

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A camera is designed much like the human eye.

A smartphone camera is composed of various parts such as a lens, infrared cut-off filter², auto focusing actuator,³ and CMOS image sensor (CIS).⁴ Among them, CIS is a key component that acts as a retina of the human eye. As shown in Figure 1, it is composed of a photodiode that converts light into electrons, a color filter where only the light of a specific wavelength can pass through, an analog/digital circuit that converts electrons into digital signals, and an image signal processor (ISP) responsible for correction and image processing.

Since the resolution, sensitivity, and signal-to-noise ratio (SNR)⁵ are determined by the CIS performance, it can be said that the image quality of the smartphone camera is determined by the CIS. Today, the CIS image quality of smartphone cameras has surpassed the level of compact cameras, and the gap from DSLRs is continually being narrowed.⁶

In terms of performance, CIS has developed in the direction of reinforcing the number of pixels and its functions. Since more detailed and clearer image quality can be obtained with the increase in the number of pixels, the competition for the higher number of pixels started from the early stage of smartphone cameras. In addition, with innovations in semiconductor micro-processing such as back side illumination (BSI)⁷ and deep trench isolation (DTI)⁸, more pixels are integrated in the same size of area, making it possible to easily capture images of tens of millions of pixels even in a general smartphone.

²Infrared cut-off filter: A device that only passes the visible light and blocks the infrared wavelength
³Auto focusing actuator: A lens driving device for autofocusing, implemented with a small motor
⁴CIS: A device that can detect light and convert it into an electrical signal with a structure of ‘Complementary Metal Oxide Semiconductor (CMOS)’ composed of different MOS integrated circuits.
Acts as an electronic film in fliming electronic devices such as smartphones and cameras with high speed and low power consumption.
⁵SNR (Signal-to-Noise Ration): Defined as 20 log (signal/noise)
⁶David Cardinal, “Smartphones vs Cameras: Closing the gap on image quality”, DXO Mark. (2020) (URL)
⁷BSI (Back Side Illumination): A technology that increases the amount of light received on a photodiode by accepting light from the rear of the sensor. When light enters from the front of the sensor, light loss occurs due to scattering by metal wiring.
⁸DTI (Deep Trench Isolation): Process technology to make barriers between physical pixels between adjacent photodiodes inside silicon to prevent signal interference between pixels.

The Latest Technology Trend in CIS is about Function, not Pixel

Nevertheless, this trend for high pixels in CIS is expected to face technical difficulties soon, and the innovation for a high level of functions centered on the ISP will be in full swing.

This is due to the limits of miniaturization of CIS pixels due to the diffraction limit⁹. CIS is a complex component that combines optical technologies such as micro lenses and semiconductor technologies such as devices and circuits. It is possible to reduce the critical dimension of electric circuits to several nanometers with the current semiconductor technology; however, since the light reception amount decreases as the pixel size decreases, the sensitivity and the signal level is reduced, resulting in the decline in SNR and the image quality degradation.

⁹Diffraction limit: A limit point in which the distance between two objects is too close, making it difficult to distinguish with an optical lens.

Figure 2. Airy disk diffraction image

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The optical system of the camera also has a physical limit where the performance is limited by the diffraction effect,¹⁰ and even if one point light source¹¹ is taken, the image formed on the CIS through the lens is spread out, as seen in Figure 2. This is called an airy disk¹²; given the wavelength (λ), focal length (f), and lens diameter (d), the distance (x) that can separate the two points is determined by the following formula:

¹⁰Diffraction effect: A phenomenon in which light proceeds in a curved path rather than a straight path when it encounters an obstacle.
¹¹Point light source: A light source that is small enough to be considered as a dot.
¹²Airy disk: A phenomenon that the image is spread out when one point light source is condensed on the CIS through the lens.

For example, for a 400nm blue point light source, even if a high-performance lens with an F (=f/d) number of 1.4 is used, the distance that can separate the two points is 0.68μm. In other words, to distinguish the two blue point light sources, the distance should be at least 0.68μm. Therefore, even if the size of CIS pixels is made smaller than this, it is difficult to expect substantial improvement in resolution. Since the size of the commercially available CIS pixel has already reached 0.7 to 0.8μm, it is necessary to develop a new optical technology to reduce the F number or a new application that can merge several fine pixels.

Another reason for this innovation is the emergence of stack sensor technology. Since the conventional sensor has a structure where pixels and circuits are implemented on the same substrate, it was essential to reduce the light-free area for the CIS size reduction. Therefore, only essential functions of analog/digital circuits were implemented and adding circuits for additional functions was very limited.

Figure 3. Left: Conventional sensor structure / Right: Stack sensor structure (right)

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On the contrary, the stack sensor has a structure where pixels and circuits are implemented on separate substrates as shown in Figure 3, and then the two substrates are connected electrically by Through Silicon Via (TSV)¹³ or hybrid bonding technology.¹⁴ Since pixels and circuits are stacked together, the circuits on the lower substrate can be used as much as the area occupied by the pixels on the upper substrate, leading that area free to use.

¹³TSV (Through Sillicon Via): An interconnecting technology that delivers electric signals through column-shaped paths that penetrate the entire silicon wafer thickness.
¹⁴Hybrid bonding technology: A process technology that bonds metal electrodes of two wafers together and connects them, instead of an electrode penetrating through a silicon wafer. It can reduce size while increasing performance.

For example, a 48 megapixel (8,000 x 6,000) sensor implemented with 1μm occupies an area of 48mm² or more on the upper substrate. If this size of area on the lower substrate can be used for the implementation of digital logic, it is possible to utilize a large space, enough to integrate a large number of high-performance microprocessors.¹⁵

In addition, the stack sensor has the advantage that an independent process can be applied to the pixels on the upper substrate and the circuits on the lower substrate. If an advanced logic process¹⁶ is applied to the lower substrate for circuits, even a complicated ISP algorithm can be implemented with low power, high density digital circuits. In other words, while the ISP of the conventional sensor only supported simple functions such as lens correction and defect correction due to the limitation of the circuit area, the ISP of the stack sensor can implement innovative algorithms such as image processing, computer vision, and artificial intelligence (AI) by using an advanced logic process.

¹⁵Microprocessor: A device that integrates the control functions of the processing unit and central processing unit into one chip
¹⁶Logic process: Semiconductor process that manufactures digital devices to process logical operations such as AND, OR, NOT

SK hynix’s CIS with Various Functions

Currently, SK hynix’s CIS has built-in image processing functions such as phase detection auto focus (PDAF), Quad pixel processing, and high dynamic range (HDR) processing, and new functions are constantly being added to it.

Figure 4. Left: Half Shield PDAF structure / Right: Paired PDAF structure

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PDAF is a function that applies the principle that human’s both eyes use to predict the distance to a subject. That is, for some pixels of the CIS, this method generates a phase difference¹⁷ by covering the left and right, as shown in the left side of Figure 4, or by placing the left and right pixels under one large micro lens as shown in the right side of Figure 4. Through this method, the ISP algorithm calculates the phase difference from the left and right images and converts it into a distance to the subject to focus quickly and accurately.

¹⁷Phase difference : The phase difference between two vibrations (wavelengths). The phase refers to the relative position of the vibration(wavelength) at a certain point.

Figure 5. Left: Output of a conventional sensor / Right: Output of a Quad sensor

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The Quad sensor has the function of placing four color filters of the same color adjacent to each other and processing them together. In dark places, four pixels are combined and processed to receive more light and in bright places, the individual pixels are separately processed by the ISP algorithm to improve resolution. Figure 5 shows two images taken with SK hynix’s 48 megapixel Quad sensor and a conventional sensor, respectively. This shows that it is possible to obtain a bright image without noise even in a dark place when using a Quad sensor, compared to when using a conventional sensor.

Figure 6. Left: Output of a conventional sensor / Right: Output of an HDR sensor

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The HDR sensor supports a function that makes a clear contrast between the bright and dark parts of an image by synthesizing multiple pixels with different sensitivity and exposure time. In particular, with the SK hynix’s CIS, image processing is performed by a built-in ISP, resulting in real-time processing and clear image quality even with a moving object. Figure 6 shows the output images with and without HDR application to the SK hynix’s CIS. Compared to the image where HDR is not used, the image using HDR restores the background clearly while maintaining the same brightness of the whole image.

Currently, SK hynix’s CIS, mainly the Black Pearl product line, is widely used in smartphone cameras and the application field is expected to expand to various fields such as bio, security, and autonomous vehicles.

Figure 7. Camera mounting location in self-driving car

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In particular, autonomous vehicles use at least ten cameras to detect their surroundings.¹⁸ To improve accuracy, various requirements such as high resolution support for distinguishing distance objects, HDR support for recognizing objects even in dark environments, and pre-processing of the ISP to reduce the computational amount of the processor must be satisfied.

In the security field, a function to compress and encrypt image signals in the CIS built-in ISP and to transmit them to an external processor is required. If the unencrypted image signal is transmitted to the outside as it is, the possibility of security vulnerability and information leakage increases. For this reason, the encryption function inside the CIS is essential.

¹⁸Peter Brown, “Breaking Down the Sensors Used in Self-Driving Cars”, Electronics 360. (2018) (URL)

The future of CIS: Information Sensor that supports advanced functions

In the future, CIS is expected to evolve into an information sensor that supports advanced additional functions, without being limited to image quality improvement.¹⁹ SK hynix’s stack sensor is already capable of embedding a simple AI hardware engine inside the ISP on the lower substrate, based on the advanced semiconductor process. Based on this, SK hynix is currently developing new machine learning-based technologies such as super resolution, color restoration, face recognition, and object recognition.

¹⁹Sungjoo Hong, “Smart Cloud and Information Sensor”, Smart Cloud Show. (2018) (URL)

Since it is possible to extract and classify various features from input images when using machine learning-based ISP technology, CIS will become a key component of information sensors that collect various information such as image information, location information, distance information, and biometric information.

In particular, to utilize CIS as an information sensor, it is necessary to take a new approach from a different perspective. This is because the quality goal of CIS is now “achieving the image quality optimized for machine algorithms”, while the goal was previously “achieving the image quality optimized for human eyes” so far.

Figure 7. Left: Less noise but misrecognized as a spotlight
Right: More noise but accurately recognized as an espresso

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According to the results of a research of Stanford University, even if a good-looking image to the human eye is created through complex image processing, it does not always mean that this image produces excellent results when computer vision algorithms are applied to it. For example, when an object recognition algorithm is applied to the coffee cup image in Figure 7, the image on the left with less noise is incorrectly recognized as a spotlight, but the image on the right with much more noise is accurately recognized as an espresso. This shows that the key function for the future CIS is to provide image quality optimized for the computer algorithm to be used.²⁰

²⁰Diamond, V. Sitzmann, S. Boyd, G. Wetzstein, F. Heide ‘Dirty Pixels: Optimizing Image Classification Architectures for Raw Sensor Data’, arXiv preprint arXiv:1701.06487.

As explained in this article, SK hynix is increasing the level of integration of the CIS pixels through the continuous development of device and process technologies and supporting various application fields through the ISP technology development. Especially, to pioneer new technology fields, it has established and is operating overseas research institutes in Japan and the United States, and various researches are actively being conducted with domestic and foreign universities through the academic-industrial collaboration. In the future, SK hynix’s CIS is expected to be utilized in various application fields including smartphone cameras to contribute to the creation of economic and social value and to grow as a key component of information sensors in the future.

The post The Visual Evolution & Innovation of Image Sensors first appeared on SK hynix Newsroom.

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