First Advisor

Erik Bodegom

Date of Publication

Fall 1-14-2019

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.) in Applied Physics

Department

Physics

Language

English

Subjects

Image converters, Signal processing, Machine learning, Electronic noise

DOI

10.15760/etd.7241

Physical Description

1 online resource (xiv, 111 pages)

Abstract

Dark current random telegraph signal (DC-RTS) is a physical phenomenon that effects the performance of solid state image sensors. Identified by meta-stable stochastic switching between two or more dark current levels, DC-RTS is an emerging concern for device scientists and manufacturers as a limiting noise source. Observed and studied in both charge coupled devices (CCDs) and complementary metal-oxide-semiconductor (CMOS) image sensors, the metastable defects inside the device structure that give rise to this switching phenomenon are known to be derived from radiation damage. An examination of the relationship between high energy photon damage and these RTS defects is presented and the results discussed. Evidence is presented which supports a second order generation mechanism for this particular class of RTS defect.

While troublesome to the image sensor community this kind of meta-stable switching, characteristic to RTS, is known to other scientific fields as an important dynamic that provides a description of those systems. Measurements of single molecule chemical reactions, for instance, produce the same general signal shape as those produced by RTS pixels. This commonality has motivated the development of a tool that can extract the key parameters of these signals, the amplitude and state lifetime. The amplitude is defined as the magnitude between two switching states while the state lifetimes are simply the mean time the system spends in the respective states. These parameters provide information on the features of these metastable systems.

It has been shown in previous RTS studies that a straightforward way to extract these parameters is to remove the Gaussian noise from the signals, leaving only the RTS transitions. This dissertation will present three methodologies that utilize noiseless reconstruction of signals for parameter extraction: convolutional filtering, wavelet denoising, and deep learning reconstruction. The capabilities of these techniques are examined quantitatively in a controlled experiment and qualitatively on data collected from a CCD image sensor, and the results compared against each other.

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Persistent Identifier

https://archives.pdx.edu/ds/psu/30810

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