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Friday, March 02, 2018

Noise in Charge Domain Sampling Readouts

MDPI Special Issue on the 2017 International Image Sensor Workshop publishes Delft University paper "Temporal Noise Analysis of Charge-Domain Sampling Readout Circuits for CMOS Image Sensors" by Xiaoliang Ge and Albert J. P. Theuwissen.

"In order to address the trade-off between the low input-referred noise and high dynamic range, a Gm-cell-based pixel together with a charge-domain correlated-double sampling (CDS) technique has been proposed to provide a way to efficiently embed a tunable conversion gain along the read-out path. Such readout topology, however, operates in a non-stationery large-signal behavior, and the statistical properties of its temporal noise are a function of time. Conventional noise analysis methods for CMOS image sensors are based on steady-state signal models, and therefore cannot be readily applied for Gm-cell-based pixels. In this paper, we develop analysis models for both thermal noise and flicker noise in Gm-cell-based pixels by employing the time-domain linear analysis approach and the non-stationary noise analysis theory, which help to quantitatively evaluate the temporal noise characteristic of Gm-cell-based pixels. Both models were numerically computed in MATLAB using design parameters of a prototype chip, and compared with both simulation and experimental results. The good agreement between the theoretical and measurement results verifies the effectiveness of the proposed noise analysis models."

3 comments:

  1. So this circuit looks like the readout method inside a micro-bolometer sensor.

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  2. How about the linearity of this arrangement please ?

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  3. Part of this paper looks like an exact repetition of Sepke's work: Noise Analysis for Comparator-Based Circuits. Nice extension and tailoring to APS, but those kind of studies are almost always overly convoluted to be practical in real-life design. In the end it all boils down to one thing: Sigma_V = K x Vn x sqrt(t)

    K is some coefficient (circuit-dependent and linked with drift and the central limit theorem under weak dependence), Vn the wide-sense-stationary noise magnitude (e.g. thermal,1/f,kTC), and you have the squareroot of time, so sigma increases with time ie. a drifting random walk. The additional expansion by adding concrete thermal, 1/f, and other noise models just complicates the picture. Add up boundary conditions, knee points, voltage ranges and such studies really becomes a problem for a superhumans... or, perhaps a SPICE engine?

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