Compact image systems bring up people's attention in the field of target recognition, surveillance, situation awareness or even photography. Conventional metrics assess image system based on image quality without considering systems' volume. More comprehensive metrics, such as General Image-Quality Equation and the Targeting Task Performance metric, incorporates all image system components from object, lenses to detector and even imaging processing algorithm. All these key factors prohibit these metrics from being applied to image system in a convenient manner. Here, we propose a simple metric, volumetric imaging efficiency, considering both image quality and volume. Only concentrate on optical lenses enables the metric being implemented onto conventional bulk optics and flat optics efficiently. Curved image sensor with monocentric lenses shows an exceptional performance based on our metric but potentially challenging in fabrication due to conventional flat substrate process. Normally, this can be done with inorganic photodetector array and perform bending as the last step or organic photodetector being directly deposited on a curved plastic substrate. Inorganic method utilizes state-of-the-art CMOS technology but the interconnects suffer great strain and stress after bending and ultimately runs the risk of device failure while organic device ensures minimum strain, but the fabrication is not compatible with CMOS technology, thus a pattern transfer method is involved for contacts deposition. Here, we introduce both techniques and addressing their challenges. For inorganic device, several interconnect deposition methods are developed and both 1D and 2D bending test are performed to test their stretchability. For organic device, without CMOS circuity, we developed a new type of photodetector, frustrated organic photodetector (F-OPD), which enables single pixel selection by biasing device in different directions. A total of 45 devices are fabricated and perform as an input of 30X30 detectors array. A variety of noise sources are discussed and applied to each pixel. The image is then restored by color leveling and 2-points or 3-points non-uniformity correction (NUC). The results are compared with original figure as a proof of concept showing the capability of the device being extended to an array.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2609 |
| Date | 01 January 2022 |
| Creators | Ma, Zhao |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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