Journal of the Society for Information Display

Following the success of SID's Display Week, the Journal of the Society for Information Display (JSID) will have multiple issues with strong contributions.

JSID is set to showcase papers from the International Display Workshops (IDW) 2024 and Eurodisplay 2024. The May issue will feature 36 distinguished papers from Display Week 2025, which represent the top 5 percent of submitted papers. The issue will be freely available online until December 2025.

Two special issues—one on artificial intelligence (AI) and machine learning (ML) and another on augmented, virtual, and mixed reality (AR/VR/MR)—are in the planning stages. We invite you to take advantage of these opportunities to remain at the forefront of innovation and insight. Explore the JSID website to review the latest updates and research pertaining to display technology. https://sid.onlinelibrary.wiley.com/journal/19383657

The authors studied the retinal projection AR display's eyebox expansion, which may cause image deviation. They propose an eyebox-extended retinal projection display with gaze-matching for AR. The system's exit pupil is a composite image combiner using three holographic optical elements (HOEs). Each HOE is positioned at a specific location on the waveguide. Simulation and experimental results have proven that the proposed system can project virtual images onto the fovea of the human eye to achieve gaze-matching.

Amorphous InGaZnO (a-IGZO) thin-film transistors exhibit significant drain current drop (DCD) degradation at high drain voltage, which has been ascribed to the impact of hot carriers on the lattice of a-IGZO films. In this study, the authors unveil that interstitial oxygen (Oi) defects also contribute to DCD degradation. It is inferred that the impact would easily break the weak interstitial oxygen bonds near hot carriers' drain region. The authors observed that introducing hydrogens effectively alleviates DCD degradation, which is attributed to hydrogen-induced transitions from the weak interstitial oxygen bonds to the strong oxygen−hydrogen bonds.

The display quality of degraded document images on E-paper often suffers from a lack of detail. The authors introduce a mapping model that converts color images into E-paper display images to address this challenge. This model leverages U-Net++ as its backbone, integrating residual connectivity and dual attention modules. A fixed-size convolutional kernel is insufficient, given the presence of varying stroke thicknesses in document images. Therefore, they propose multibranch channels and spatial attention modules, which combine the selective kernel network with a spatial attention mechanism to select the appropriate convolutional kernel size adaptively based on font size. The authors also tested the mapped images on a custom E-paper display platform, confirming the proposed method's superior performance.

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