Roadmap for CMOS image sensors: Moore meets Planck and Sommerfeld

Catrysse, Peter B.; Wandell, Brian A.

doi:10.1117/12.592483

Cited by 50 publications

(30 citation statements)

References 17 publications

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“…ISET is an integrated suite of Matlab software routines designed to simulate the entire image processing pipeline of a digital camera [1]. The analysis described in this paper extends the work previously published by Chen et al [2] and Catrysse and Wandell [3] who also simulated the optical blurring, sensor transduction and image processing pipelines for CMOS imaging sensors with the same dye size but varying pixel sizes. We extend their analysis to color imaging sensors and consider the role other sensor parameters, such as quantum efficiency, voltage swing, conversion gain and different noise sources, as well as pixel size and fill factor, have upon the spatial resolution and light sensitivity of an imaging sensor.…”

Section: Introductionmentioning

confidence: 93%

Resolution and light sensitivity tradeoff with pixel size

2006

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When the size of a CMOS imaging sensor array is fixed, the only way to increase sampling density and spatial resolution is to reduce pixel size. But reducing pixel size reduces the light sensitivity. Hence, under these constraints, there is a tradeoff between spatial resolution and light sensitivity. Because this tradeoff involves the interaction of many different system components, we used a full system simulation to characterize performance. This paper describes system simulations that predict the output of imaging sensors with the same dye size but different pixel sizes and presents metrics that quantify the spatial resolution and light sensitivity for these different imaging sensors.

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Section: Introductionmentioning

confidence: 93%

Resolution and light sensitivity tradeoff with pixel size

2006

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“…Assuming that the surface of object(s) in the scene (depicted by the camera system) is Lambertian, photometric exposure H can be described in a similar way to Cartrysse et al 12 by…”

Section: Simulations and Predictive Performancementioning

confidence: 99%

Does resolution really increase image quality?

Tisse¹,

Guichard²,

Cao³

2008

SPIE Proceedings

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A general trend in the CMOS image sensor market is for increasing resolution (by having a larger number of pixels) while keeping a small form factor by shrinking photosite size. This article discusses the impact of this trend on some of the main attributes of image quality. The first example is image sharpness. A smaller pitch theoretically allows a larger limiting resolution which is derived from the Modulation Transfer Function (MTF). But recent sensor technologies (1.75µm, and soon 1.45µm) with typical aperture f/2.8 are clearly reaching the size of the diffraction blur spot. A second example is the impact on pixel light sensitivity and image sensor noise. For photonic noise, the Signal-to-Noise-Ratio (SNR) is typically a decreasing function of the resolution. To evaluate whether shrinking pixel size could be beneficial to the image quality, the tradeoff between spatial resolution and light sensitivity is examined by comparing the image information capacity of sensors with varying pixel size. A theoretical analysis that takes into consideration measured and predictive models of pixel performance degradation and improvement associated with CMOS imager technology scaling, is presented. This analysis is completed by a benchmarking of recent commercial sensors with different pixel technologies.

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“…15 A standard solution, used in sensors for digital imaging applications, consists of shifting the (identical) micro-lenses inwards. 2 One way in which nanophotonics can provide improved functionality for micro-lenses is through nanopatterning of the layers in a CMOS process. Onozawa and co-workers, for example, developed digital micro-lenses (DMLs) in which an effective refractive index is realized by variation of the sub-wavelength separations between concentric SiO 2 ring walls.…”

Section: Planar Nanophotonic Micro-lensmentioning

confidence: 99%

Integration of optical functionality for image sensing through sub-wavelength geometry design

Catrysse

2015

SPIE Proceedings

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Integration and pixel scaling are enabling trends for low-cost, small form-factor, light-weight, and low-power camera systems across all optical frequency bands. Integration allows on-chip implementations of optical functionalities, e.g., polarization, color and spectral selectivity, which were previously achieved using bulky external components (e.g., prisms, filter wheels). Wavelength-size pixels require focusing and guiding of light at the pixel-level (e.g., micro-lenses, light guides), which was not necessary for larger pixels. On-chip integration of optical functionalities and pixel-level light control is often based on miniaturized versions of conventional optical components. These components derive their functionality from material properties or shapes, but do not lend themselves well to integration or scale to wavelengthsize pixels. Here, I present an overview of innovative integrated optical devices that overcome the problems associated with the integration and scaling of conventional components. They are enabled by scaling of imager and focal plane array technology, and by advanced nanofabrication. Wafer-scale processing and nanofabrication have made nano-scale patterning possible, while recent discoveries regarding the optical properties of nano-patterned structures have opened up important opportunities to develop ultra-compact photonic devices. I discuss design implementations for the visible (VIS) and infrared (IR) wavelength bands, including VIS integrated color pixels (ICPs) in a complementary metal-oxide semiconductor (CMOS) technology with geometry-based color filters, micro-lens functionality based on planar nanoaperture designs, and metal-based spectral filters for mid-wave IR (MWIR) multispectral imaging that can be implemented using standard nanofabrication methods. The devices are planar and/or ultra-thin, rely on processcompatible materials only, and derive their functionality from sub-wavelength geometry design.

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Roadmap for CMOS image sensors: Moore meets Planck and Sommerfeld

Cited by 50 publications

References 17 publications

Resolution and light sensitivity tradeoff with pixel size

Resolution and light sensitivity tradeoff with pixel size

Does resolution really increase image quality?

Integration of optical functionality for image sensing through sub-wavelength geometry design

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