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Proceedings Paper

Photocurrent estimation for a self-reset CMOS image sensor
Author(s): Xinqiao Liu; Abbas El Gamal
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Paper Abstract

CMOS image sensors are capable of very high frame rate non- destructive readout. This capability and the potential of integrating memory and signal processing with the sensor on the same chip enable the implementation of many still and video imaging applications. An important example is dynamic range extension, where several images are captured during a normal exposure time - shorter exposure time images capture the brighter areas of the scene while longer exposure time images capture the darker areas of the scene. These images are then combined to form a high dynamic range image. Dynamic range is extended at the high end by detecting saturation, and at the low end using linear estimation algorithms that reduce read noise. With the need to reduce pixel size and integrate more functionality with the sensor, CMOS image sensors need to follow the CMOS technology scaling trend. Well capacity, however, decreases with technology scaling as pixel size and supply voltages are reduced. As a result, SNR decreases potentially to the point where even peak SNR is inadequate. In this paper, we propose a self-reset pixel architecture, which when combined with multiple non-destructive captures can increase peak SNR as well as enhance dynamic range. Under high illumination, self-resetting 'recycles' the well during integration resulting in higher effective well capacity, and thus higher SNR. A recursive photocurrent estimation algorithm that takes into consideration the additional noise due to self- resetting is described. Simulation results demonstrate the SNR increase throughout the enhanced photocurrent range with 10dB increase in peak SNR using 32 captures.

Paper Details

Date Published: 24 April 2002
PDF: 9 pages
Proc. SPIE 4669, Sensors and Camera Systems for Scientific, Industrial, and Digital Photography Applications III, (24 April 2002); doi: 10.1117/12.463435
Show Author Affiliations
Xinqiao Liu, Stanford Univ. (United States)
Abbas El Gamal, Stanford Univ. (United States)


Published in SPIE Proceedings Vol. 4669:
Sensors and Camera Systems for Scientific, Industrial, and Digital Photography Applications III
Nitin Sampat; John Canosa; Morley M. Blouke; John Canosa; Nitin Sampat, Editor(s)

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