In many applications, such as development and testing of image processing algorithms, it is often necessary to simulate images containing realistic noise from solid-state photosensors. A high-level model of CCD and CMOS photosensors based on a literature review is formulated in this paper. The model includes photo-response non-uniformity, photon shot noise, dark current Fixed Pattern Noise, dark current shot noise, offset Fixed Pattern Noise, source follower noise, sense node reset noise, and quantisation noise. The model also includes voltage-to-voltage, voltage-to-electrons, and analogue-to-digital converter non-linearities. The formulated model can be used to create synthetic images for testing and validation of image processing algorithms in the presence of realistic images noise. An example of the simulated CMOS photosensor and a comparison with a custom-made CMOS hardware sensor is presented. Procedures for characterisation from both light and dark noises are described. Experimental results that confirm the validity of the numerical model are provided. The paper addresses the issue of the lack of comprehensive high-level photosensor models that enable engineers to simulate realistic effects of noise on the images obtained from solid-state photosensors.

High-level numerical simulations of noise in CCD and CMOS photosensors: review and tutorial.

A 128 x 128 element CMOS active pixel image sensor (APS) with on-chip timing, control, and.

128 x 128 CMOS Photodiode-Type Active Pixel Sensor with On-Chip Timing, Control and Signal

The dosimetric response of an active pixel sensor is analyzed. heavy ions are seen to damage the pixel in much the same way as gamma radiation. The probability of a hot pixel is seen to exhibit behavior that is not typical with other microdose effects.

Hot Pixel Generation in Active Pixel Sensors: Dosimetric and Micro-dosimetric Response

As solid-state image sensors become ubiquitous in sensing, control and photography products, their long-term reliability becomes paramount. This thesis experimentally examines the nature of in-field faults and demonstrates two combined hardware-software approaches for detecting and mitigating them. Characterization experiments found that most tested commercial cameras developed hot pixels that create image bright spots and degrade dynamic range. Faults appear spatially point-like and uniformly distributed, and they develop continually over time. Silicon displacement damage, induced by terrestrial cosmic rays, is the likely cause. A fault tolerant active pixel sensor is developed to isolate hot defects to a portion of the pixel, enabling software algorithms to correct the faults without sacrificing dynamic range. Experimentally-emulated hot pixels can be corrected within ±5% error. A new statistical software approach is developed to identify and calibrate stuck and abnonnal-sensitivity faults from only regular photographs. Monte Carlo simulations verify the detection accuracy in complex environments.

Characterization and avoidance of in-field defects in solid-state image sensors

An image sensor system-level pixel-to-pixel photo-response non-uniformity (PRNU) error tolerance method is presented
in this paper. A scheme is developed to determine sensor PRNU acceptability and corresponding sensor application
categorization. Many low-cost imaging systems utilize CMOS imagers with integrated on-chip digital logic for
performing image processing and compression. Due to pixel geometry and substrate material variations, the light
sensitivity of pixels will be non-uniform (PRNU). Excessive variation in the sensitivity of pixels is a significant cause of
the screening rejection for these image sensors. The proposed testing methods in this paper use the concept of
acceptable degradation applied to the camera system processed and decoded images of these sensors. The analysis
techniques developed in this paper give an estimation of the impact of the sensor's PRNU on image quality. This
provides the ability to classify the sensors for different applications based upon their PRNU distortion and error rates.
The human perceptual criteria is used in the determination of acceptable sensor PRNU limits. These PRNU thresholds
are a function of the camera system's image processing (including compression) and sensor noise sources. We use a
Monte Carlo simulation solution and a probability model-based simulation solution along with the sensor models to
determine PRNU error rates and significances for a range of sensor operating conditions (e.g., conversion gain settings,
integration times). We develop correlations between industry standard PRNU measurements and final processed and
decoded image quality thresholds. The results presented in this paper show that the proposed PRNU testing method can
reduce the rejection rate of CMOS sensors. Comparisons are presented on the sensor PRNU failure rates using industry
standard testing methods and our proposed methods.

Photo-response non-uniformity error tolerance testing methodology for CMOS imager systems

This paper presents a qualification methodology on imaging sensors. In addition to overall chip reliability characterization based on sensor's overall figure of merit, such as Dark Rate, Linearity, Dark Current Non-Uniformity, Fixed Pattern Noise and Photon Response Non-Uniformity, a simulation technique is proposed and used to project pixel reliability. The projected pixel reliability is directly related to imaging quality and provides additional sensor reliability information and performance control.

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M. Petkov

Papers

A chip and pixel qualification methodology on imaging sensors