Three-phase, backside illuminated 500&amp;amp;#215;500 CCD imager

Blouke, Morley M.; Breitzmann, J.F.; Hall, Joseph E.

doi:10.1109/isscc.1978.1155771

Cited by 4 publications

(2 citation statements)

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“…In this context, it always has to be kept in mind that the individual pixels are not sensitive to light over their whole area but also contain insensitive regions resulting from the circuitry within each pixel. Nevertheless, by using techniques like backside illumination [11], [12] and / or microlenses [13]- [17] a fill-factor of nearly 100% can be achieved. Using this, one gets an array of rectangular light sensitive areas and the acquisition process can be regarded as an integration of the incident light on identical areas located on the twodimensional regular grid.…”

Section: Proposed Sensor Layoutmentioning

confidence: 99%

“…Looking at state-of-the-art image sensors, it becomes obvious that one always tries to make most of the area sensitive to light and the objective hence is to bring the fill-factor close to one. This can be achieved for example by applying techniques like backside illumination [11], [12] or the use of microlenses [13]- [17]. However, these techniques again lead to a regular sensing and therewith the resolution is limited by aliasing.…”

Section: Introductionmentioning

confidence: 99%

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Increasing Imaging Resolution by Non-Regular Sampling and Joint Sparse Deconvolution and Extrapolation

Seiler,

Jonscher,

Ussmueller

et al. 2022

Preprint

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Increasing the resolution of image sensors has been a never ending struggle since many years. In this paper, we propose a novel image sensor layout which allows for the acquisition of images at a higher resolution and improved quality. For this, the image sensor makes use of non-regular sampling which reduces the impact of aliasing. Therewith, it allows for capturing details which would not be possible with state-ofthe-art sensors of the same number of pixels. The non-regular sampling is achieved by rotating prototype pixel cells in a nonregular fashion. As not the whole area of the pixel cell is sensitive to light, a non-regular spatial integration of the incident light is obtained. Based on the sensor output data, a high-resolution image can be reconstructed by performing a deconvolution with respect to the integration area and an extrapolation of the information to the insensitive regions of the pixels. To solve this challenging task, we introduce a novel joint sparse deconvolution and extrapolation algorithm. The union of non-regular sampling and the proposed reconstruction allows for achieving a higher resolution and therewith an improved imaging quality.

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Section: Proposed Sensor Layoutmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Increasing Imaging Resolution by Non-Regular Sampling and Joint Sparse Deconvolution and Extrapolation

Seiler,

Jonscher,

Ussmueller

et al. 2022

Preprint

View full text Add to dashboard Cite

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Solid‐State Image Sensors

Fowler

2015

Handbook of Digital Imaging

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This chapter presents an overview of solid‐state image sensor technology. It starts with a historical review of CCDs and MOS image sensors since the late 1960s. Then, the basic theory of silicon photon detection is presented. Pixel‐level optics is described next, followed by an analysis of photodetector quantum efficiency and modulation transfer function. Then, the various noise sources in solid‐state image sensors are reviewed, followed by a discussion about CCD and CMOS image sensor readout architectures. Finally, various methods for extending the dynamic range of solid‐state image sensors are reviewed.

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