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HAL-CEA
Conference object . 2019
Data sources: HAL-CEA
https://doi.org/10.23919/date....
Article . 2019 . Peer-reviewed
Data sources: Crossref
DBLP
Conference object . 2024
Data sources: DBLP
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Advanced 3D Technologies and Architectures for 3D Smart Image Sensors

Authors: Pascal Vivet; Gilles Sicard; Laurent Millet; Stéphane Chevobbe; Karim Ben Chehida; Luis Angel Cubero; Monte Alegre; +10 Authors

Advanced 3D Technologies and Architectures for 3D Smart Image Sensors

Abstract

Image Sensors will get more and more pervasive into their environment. In the context of Automotive and IoT, low cost image sensors, with high quality pixels, will embed more and more smart functions, such as the regular low level image processing but also object recognition, movement detection, light detection, etc. 3D technology is a key enabler technology to integrate into a single device the pixel layer and associated acquisition layer, but also the smart computing features and the required amount of memory to process all the acquired data. More computing and memory within the 3D Smart Image Sensors will bring new features and reduce the overall system power consumption. Advanced 3D technology with ultra-fine pitch vertical interconnect density will pave the way towards new architectures for 3D Smart Image Sensors, allowing local vertical communication between pixels, and the associated computing and memory structures. The presentation will give an overview of recent 3D technologies solutions, such as Hybrid Bonding technology and the Monolithic 3D CoolCube™ technology, with respective 3D interconnect pitch in the order of 1 μm and l00nm. Recent 3D Image Sensors will be presented, showing the capability of 3D technology to implement fine grain pixel acquisition and processing with ultra-high speed image acquisition and tile-based processing. As further perspectives, multi-layer 3D image sensor based on events and spiking will reduce power consumption with new detection and learning processing capabilities.

Country
France
Keywords

[SPI] Engineering Sciences [physics]

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    selected citations
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    9
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Top 10%
Top 10%
Green