A 32*32 two-dimensional photodetector array using a-Si pin photodiodes and poly-Si TFTs integrated on a transparent substrate

Okamura,; Kimura,; Shirai,; Yamauchi,

doi:10.1109/iedm.1992.307452

Cited by 3 publications

(1 citation statement)

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“…Cover glass above the panel is used to support and protect the panel. The sensor, known as PIN, consists of a p-type and n-type silicon, sandwiching a layer of amorphous silicon as the photosensitive material, converting light into electrons [22]. TFT circuits, used as pixel driving scheme for sensors, were fabricated by the low-temperature polycrystalline silicon (LTPS) process.…”

Section: Device and Optical Methodsmentioning

confidence: 99%

An Image Unmixing and Stitching Deep Learning Algorithm for In-Screen Fingerprint Recognition Application

Chen,

Feng,

et al. 2023

Electronics

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The market share of organic light-emitting diode (OLED) screens in consumer electronics has grown rapidly in recent years. In order to increase the screen-to-body ratio of OLED phones, under-screen or in-screen fingerprint recognition is a must-have option. Current commercial hardware schemes include adhesive, ultrasonic, and under-screen optical ones. No mature in-screen solution has been proposed. In this work, we designed and manufactured an OLED panel with an in-screen fingerprint recognition system for the first time, by integrating an active sensor array into the OLED panel. The sensor and display module share the same set of fabrication processes when manufactured. Compared with the current widely commercially available under-screen schemes, the proposed in-screen solution can achieve a much larger functional area, better flexibility, and smaller thickness, while significantly reducing module cost. A point light source scheme, implemented by lighting up a single or several adjacent OLED pixels, instead of a conventional area source scheme as in the CMOS image sensor, or a CIS-based solution, has to be adopted since the optical distance is not long enough due to the integration. We designed a pattern for the point light sources and developed an optical unmixing network model to realize the unmixing and stitching of images obtained by each point light source at the same exposure time. After training, data verification of this network model shows that this deep learning algorithm outputs a stitched image of large area and high quality, where FRR = 0.7% given FAR = 1:50 k. In despite of a poorer quality of raw images and a much more complex algorithm compared with current commercial solutions, the proposed algorithm still obtains results comparable to peer studies, proving the effectiveness of our algorithm. Thus, the time required for fingerprint capture in our in-screen scheme is greatly reduced, by which one of the main obstacles for commercial application is overcome.

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Section: Device and Optical Methodsmentioning

confidence: 99%