Journal of the Society for Information Display最新文献

Accurate segmentation of surface defects is essential for automated inspection in modern display panel manufacturing. Although recent studies have primarily focused on image-level anomaly detection, pixel-level anomaly segmentation remains underexplored due to the lack of annotated defect data. In this paper, we propose AnoSeg, a novel anomaly segmentation framework designed to directly generate accurate anomaly maps without requiring real defective samples. AnoSeg integrates three key techniques: (1) a hard augmentation strategy for synthesizing defect-like anomalies from normal images, (2) self-supervised learning with combined pixel-wise and adversarial losses to enhance segmentation quality, and (3) coordinate channel concatenation to incorporate spatial priors relevant to defect localization in structured panel layouts. By training on only normal and synthetically augmented data, AnoSeg learns to segment anomalous regions robustly and generalizes well to unseen defects. Additionally, the resulting anomaly maps can be leveraged to improve conventional anomaly detection performance. Experimental results on the MVTec Anomaly Detection (MVTec-AD) benchmark demonstrate that AnoSeg outperforms existing state-of-the-art methods in both segmentation accuracy and detection performance, as measured by intersection over union (IoU) and AUROC metrics. These results suggest the practical applicability of AnoSeg for high-precision surface inspection in intelligent display manufacturing systems.

Large-area, high-resolution organic light-emitting diode (OLED) displays require high-performance oxide thin-film transistors (TFTs) with excellent mobility and stability, as well as little threshold voltage shift, especially for G8.5+ OLED production lines. Our previous work demonstrated rare-earth doped oxides are ideal channel layer materials for TFTs due to their superior mobility and stability. However, scaling up TFT production from pilot lines to mass manufacturing poses significant challenges, largely because of the complexity of multi-element composite materials and intricate processes. In this study, an end-to-end graph convolutional network (GCN) model is employed to optimize TFT production processes. By encoding process parameters as graph nodes and features, the GCN effectively predicts critical drain current–gate voltage (I–V) characteristics, enabling precise calculation of electron mobility, subthreshold slope, and threshold voltage. The model achieves a mean absolute percentage error of 19.9% on validation data. Based on the optimized parameters, a TFT device with properties including a mobility of 37.8 cm²/Vs, a threshold voltage of 0.9 V, and a subthreshold slope of 0.62 V/dec was successfully produced. These results highlight the potential of GCN-based models to address the complexities of TFT mass production, providing a powerful tool for process optimization and performance enhancement.

InGaN-based quantum wells (QWs) integrated on arbitrary-shaped 3D GaN templates are promising light sources for ultra-high-resolution micro-LED displays. In this paper, we first demonstrate that the emissions from InGaN QWs on polyhedral-shaped GaN templates are broader than those from planar QWs. Microscopic observations by scanning electron microscopy and cathodoluminescence spectroscopy reveal that the broad emission is due to surface undulations formed during the crystal regrowth of the QW regions on the GaN templates. To suppress the formation of undulations, we subject 3D GaN templates to chemical mechanical polishing before the regrowth. This method successfully reduces the emission spectral width by $�\sim$20% and can be a standard process for the growth integration of multicolor micro-LEDs.

Visual display systems are widely utilized in simulation training systems due to their ability to provide users with realistic visual representations. To achieve a more immersive display experience, these systems typically expand the field of view by combining multiple display devices. This paper proposes a novel spherical dome display system based on LED technology, designed to achieve a full-field, high-definition spherical display. The study focuses on the display driving mechanism of the dome, introducing a channel segmentation method to enable efficient driving. To address the geometric distortion that occurs when planar images are projected onto a spherical surface, a targeted geometric correction method is proposed. The theoretical principles and correction process are explained in detail. Through algorithm simulations and experimental validation, the proposed channel segmentation and geometric correction methods effectively reduce the number of driving channels required for the LED spherical display, decrease image processing complexity, and simplify hardware implementation. The corrected images are free from distortion and deformation, with smooth, misalignment-free transitions between channels. These methods ensure the proper functionality of the proposed LED spherical display, thereby supporting its widespread application.

Electrowetting display (EWD) is a new reflective “paper-like” display technology based on fast response microfluidic manipulation technology. It has the advantages of low energy consumption, visual health, and flexibility. However, EWDs are often plagued by oil splitting phenomena and charge trapping effects during a driving process, seriously affecting their display performance. Therefore, a new multi-stage reset driving scheme based on a sawtooth function was proposed, which could effectively solve these defects. It consisted of a shrinking stage and a stabilizing stage. First, the proposed driving scheme was driven by a negative voltage by analyzing the voltage characteristic curve. Second, based on the splitting theory, an exponential function was applied to make the pixel's oil rupture quickly. Then, a sawtooth function was applied to enable oil to achieve secondary fusion during the cyclic driving process, effectively reducing the dispersed residual oil in pixels. Finally, a reset signal was introduced to release captured charges and suppress oil backflow. Experimental results showed that the response time was reduced by 25% compared with the exponential function driving scheme. Compared with the negative PWM pulse modulation driving scheme, the average brightness was improved by 9.5%.

The mura defects of thin-film transistor liquid crystal display (TFT-LCD) panels exhibit characteristics of low contrast and random positioning, resulting in issues such as low accuracy in defect identification and poor user experience. This paper proposes a lightweight YOLO-SPPAM network. Based on the spatial pyramid pooling (SPP) module belonging to YOLOX, the newly constructed spatial pyramid pooling attention (SPPA) module allows the network to focus on salient target regions, enhancing the model's ability to perceive crucial features. This paper introduces augmentable convolutional block attention module (ACBAM) to obtain parallel dual-channel attention by parallel processing of channel attention and spatial attention. The paper replaces ordinary convolutions in down sampling with fine-grained separable convolution module (FGSCM). Qualitative and quantitative comparison experiments with state-of-the-art algorithms on a self-made TFT-LCD mura defects dataset demonstrate that YOLO-SPPAM outperforms in terms of accuracy and speed, meeting the real-time requirements of TFT-LCD defect detection tasks.

We developed a new Riemannian color difference metric for static spatial sinusoidal color variations. With this metric, one can predict, in any direction of the color space, the detection threshold of sinusoidal color variations. The metric includes the angular size, spatial frequency, luminance, and chromaticity of the grating. The metric is based on the Riemannian color difference metric for split fields that we developed earlier and models for contrast sensitivity functions of the isolated achromatic, red–green, and blue–yellow detection mechanisms. We validated the models of the contrast sensitivity functions against various datasets. The color difference metric was validated against datasets of isoluminous chromatic Gabor gratings at various spatial frequencies and various color centers. We found adequate agreement with these datasets. The results are presented in the CIELAB $�(�{�a�}^{�\ast �}�,�{�b�}^{�\ast �}�)$ plane.