A deep diffractive neural network (D2NN) is a fast optical computing structure that has been widely used in image classification, logical operations, and other fields. Computed tomography (CT) imaging is a reliable method for detecting and analyzing pulmonary nodules. In this paper, we propose using an all-optical D2NN for pulmonary nodule detection and classification based on CT imaging for lung cancer. The network was trained based on the LIDC-IDRI dataset, and the performance was evaluated on a test set. For pulmonary nodule detection, the existence of nodules scanned from CT images were estimated with two-class classification based on the network, achieving a recall rate of 91.08% from the test set. For pulmonary nodule classification, benign and malignant nodules were also classified with two-class classification with an accuracy of 76.77% and an area under the curve (AUC) value of 0.8292. Our numerical simulations show the possibility of using optical neural networks for fast medical image processing and aided diagnosis.

The low–temperature poly–Si oxide (LTPO) backplane is realized by monolithically integrating low–temperature poly–Si (LTPS) and amorphous oxide semiconductor (AOS) thin–film transistors (TFTs) in the same display backplane. The LTPO–enabled dynamic refreshing rate can significantly reduce the display’s power consumption. However, the essential hydrogenation of LTPS would seriously deteriorate AOS TFTs by increasing the population of channel defects and carriers. Hydrogen (H) diffusion barriers were comparatively investigated to reduce the H content in amorphous indium–gallium–zinc oxide (a–IGZO). Moreover, the intrinsic H–resistance of a–IGZO was impressively enhanced by plasma treatments, such as fluorine and nitrous oxide. Enabled by the suppressed H conflict, a novel AOS/LTPS integration structure was tested by directly stacking the H–resistant a–IGZO on poly–Si TFT, dubbed metal–oxide–on–Si (MOOS). The noticeably shrunken layout footprint could support much higher resolution and pixel density for next–generation displays, especially AR and VR displays. Compared to the conventional LTPO circuits, the more compact MOOS circuits exhibited similar characteristics.

Organic light-emitting diodes (OLEDs) are promising for displays and lighting technologies because of their excellent advantages, such as high efficiency, high luminance, low power consumption, light weight, and flexibility. In recent years, ultrathin emitting nanolayers (UENs) have been used to develop OLEDs without the doping technique, which can simplify device structure, reduce material loss, achieve good exciton utilization, and realize comparable performance to doped devices such as the external quantum efficiency of 28.16%, current efficiency of 63.84 cd/A, and power efficiency of 76.70 Lm/W for white OLEDs. In this review, we comprehensively summarize the recent progress in the field of UEN-based OLEDs. Firstly, the host–guest-doped OLEDs and doping-free UEN-based OLEDs are compared. Then, various effective approaches for designing UEN-based OLEDs are presented, including both monochromatic and white devices. In particular, the properties of materials, the design of device structures, and the main working mechanisms of UEN-based OLEDs are highlighted. Finally, an outlook on the future development of UEN-based OLEDs is provided.

Flat panel displays are electronic displays that are thin and lightweight, making them ideal for use in a wide range of applications, from televisions and computer monitors to mobile devices and digital signage. The Thin-Film Transistor (TFT) layer is responsible for controlling the amount of light that passes through each pixel and is located behind the liquid crystal layer, enabling precise image control and high-quality display. As one of the important parameters to evaluate the display performance, the faster response time provides more frames in a second, which benefits many high-end applications, such as applications for playing games and watching movies. To further improve the response time, the single-pixel charging efficiency is investigated in this paper by optimizing the TFT dimensions in gate driver circuits in active-matrix liquid crystal displays. The accurate circuit simulation model is developed to minimize the signal’s fall time ($T_f$) by optimizing the TFT width-to-length ratio. Our results show that using a driving TFT width of 6790 $\mathsf{\mu }\mathrm{m}$ and a reset TFT width of 640 $\mathsf{\mu }\mathrm{m}$ resulted in a minimum $T_f$ of 2.6572 $\mathsf{\mu }\mathrm{s}$, corresponding to a maximum pixel charging ratio of 90.61275%. These findings demonstrate the effectiveness of our optimization strategy in enhancing pixel charging efficiency and improving display performance.