Dedicated Process Architecture and the Characteristics of 1.4 ¿m Pixel CMOS Image Sensor with 8M Density

Moon, Cheol; Shin, Jong-Cheol; Kim, Jin-Ho; Lee, Yun Ki; Cho, Young-Joon; Yu, Yu-Yeon; Hwang, Sung Ho; Park, Byung Jun; Kim, Hwang-Yoon; Lee, Seok-Ha; Jung, Jongwan; Cho, Seong Ho; Lee, Kangbok; Koh, K.; Lee, Duck‐Hyung; Kim, Kinam

doi:10.1109/vlsit.2007.4339728

Cited by 6 publications

(1 citation statement)

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“…Blue/Green/Red (RGB) color sensing is prevalent in a wide range of applications, including imaging, charge-coupled device (CCD) camera, environmental surveillance and biological sensing [1][2][3][4][5][6], which are always in pursuit of more compact and more efficient color sensing configuration to achieve higher imaging resolution, faster detection and more flexibility, catering to the needs of wearable and soft electronics. Conventional RGB color sensors in CCD camera use Bayer mosaic matrix array with 4 photodetectors and 4 filters (2 for green, 1 for red and 1 for blue) for a single color unit, as depicted schematically in figure 1(a), which is a fundamental limit to shrink the unit size further, reduce the cost and adapt to flexible or curved surfaces [7][8][9][10][11]. To break this limit, also to avoid color aliasing in mosaic imaging [12][13][14], filter-less stacked PIN/PIN junction sensors were proposed, where the different incident wavelengths get absorbed at different penetration depths in stacked PIN junctions (see figure 1(b)) [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Bias-selected full Red/Green/Blue color sensing and imaging based on inversely stacked radial PINIP junctions

Zhang¹,

Cao²,

Zhang³

et al. 2020

Nano Futures

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Seeking an ultra-compact new architecture or strategy to achieve filter-less Red/Green/Blue (RGB) color sensing is critical for developing a wide range of high-resolution, low-cost and flexible color discrimination and imaging applications. In this work, a new inversely stacked biomimetic radial junction (RJ) PINIP photodetector, comprising of coaxial multilayers of intrinsic and doped hydrogenated amorphous Si (a-Si:H) thin films coated over silicon nanowires (SiNWs), is demonstrated, which can accomplish bias-selected and tunable spectrum responses to the R, G and B color bands, mimicking the response mechanism in human eyes. Compared to the Bayer matrix sensors, the width of a fundamental RJ-PINIP sensing unit is reduced from ∼3 μm to <500 nm, with an absorber i-layer thinned substantially from ∼1 μm to only ∼50 nm, which is particularly important to achieve a faster photo response and suppress the instability issues in a-Si:H absorber. Notably, the 3D RJ-PINIP sensors can now be easily constructed upon flexible aluminum foils (AF) and operate with a convenient two-terminal connection, which is a prerequisite for scalable matrix device implementation. As a prototype demo, the RGB color sensing and reconstruction functionalities are verified in a 125-RGB color imaging with 520*700 pixels, highlighting their unique potential for developing ultra-compact and flexible RGB imaging applications.

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Section: Introductionmentioning

confidence: 99%

Bias-selected full Red/Green/Blue color sensing and imaging based on inversely stacked radial PINIP junctions

Zhang¹,

Cao²,

Zhang³

et al. 2020

Nano Futures

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show abstract

Optics for Digital Imaging

Catrysse

2015

Handbook of Digital Imaging

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Digital imaging has become the predominant way to capture, process, store, and share images and photographs. The images in digital imaging systems are formed by an optical system composed of two equally important subsystems: the imaging optics and the pixel optics. In this chapter, I introduce, describe, and analyze these systems in detail since they both contribute in forming the optical images and are part of any digital imaging system, whether used for consumer, professional, industrial, and even scientific applications. By placing them on equal footing, I will illustrate the intricate interplay between them in accomplishing the goal of the optical system in digital imaging. I start by introducing the entire optical system in Section 1 by defining the imaging and pixel optics subsystems in terms of their optical components. In Section 2, I establish the optics fundamentals as they apply to digital imaging by reviewing the different languages used in the description of the imaging optics and pixel optics. This section provides the foundation for the remainder of the chapter. The concepts, therefore, apply to current and any future systems. Specifically, I describe the use of geometrical optics, radiometry, physical optics, and electromagnetic field optics. The first three are used in the analysis and design of the imaging optics subsystem, while the last is necessary to design, analyze, and optimize the pixel optics subsystem. Next, I apply these optical languages to the design and analysis of the optical system in digital imaging in Section 3. Here, I cover established practices and methods in light of the fact that the subsystems serve different purposes. The purpose of the imaging optics is to create beautiful images, while the pixel optics are engineered in an attempt to waste as little as possible of the photons incident on the pixels of the solid‐state image sensor. The design and the metrics used to assess their performance reflect these differing goals. In Section 4, I briefly touch on digital imaging system simulation and prediction, which is described in greater detail in Image Simulation on Image Simulation, before moving on to design considerations for optical systems in Section 5. Here, I discuss the role that pixel scaling has on imaging and pixel optics design, which is a very timely aspect of optical system design for digital imaging. In this context, I stress the important of capturing every photon incident on a pixel, which in turn motivates the concept of flux‐invariant or photon‐invariant scaling. This sets the stage for an analysis of the microlens performance limits, a study of optical confinement methods for light inside pixels, and a review of backside illumination technology. Finally, I highlight the emerging trends in optical systems for digital imaging in Section 6. As specific illustrations, I point to a few key developments in nanophotonics and computational imaging that have the potential for a significant impact on the future of optical systems.

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Metasurfaces and Multispectral Imaging

Beckett

Unnithan

2021

Progress in Optical Science and Photonics

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Dedicated Process Architecture and the Characteristics of 1.4 ¿m Pixel CMOS Image Sensor with 8M Density

Cited by 6 publications

References 0 publications

Bias-selected full Red/Green/Blue color sensing and imaging based on inversely stacked radial PINIP junctions

Bias-selected full Red/Green/Blue color sensing and imaging based on inversely stacked radial PINIP junctions

Optics for Digital Imaging

Metasurfaces and Multispectral Imaging

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