arXiv subjects

Zhiyuan Niu

Publications and source records attributed to Zhiyuan Niu.

4 recordsLinked to original sources

arXiv2026

Holographic EUV Lithography at 40 nm Resolution

Extreme ultraviolet (EUV) lithography is the cornerstone of the fabrication of advanced integrated circuits at the 7-nm node and beyond, but its reliance on multi-element reflective projection optics makes it inaccessible for small-scale research and prototyping. EUV interference lithography (EUV-IL) provides a lensless alternative but is intrinsically restricted to periodic structures. Here we demonstrate EUV holographic lithography (EUV-HL) as a lensless route to arbitrary, non-periodic, curvilinear patterning at the EUV wavelength of 13.5 nm. We introduce an inverse-design framework for computer-generated holograms that captures the dominant physical effects of EUV mask diffraction within a shift-invariant convolution model that is tractable for full mask layouts. Using this framework, we design and fabricate transmissive holographic masks by direct-write electron-beam lithography in hydrogen silsesquioxane, expose them with synchrotron-generated EUV radiation, and print target layouts with critical dimensions down to 40 nm, nearly an order of magnitude finer than the previous state of the art in EUV-HL. The demonstrated combination of sub-50 nm resolution, curvilinear design freedom, and a lensless optical setup establishes EUV-HL as a uniquely flexible tool for nanostructure prototyping at EUV wavelengths, and provides a natural pathway to non-periodic pattern prototyping at beyond-EUV (BEUV) wavelengths, which is currently inaccessible to interference-based methods.

physics.optics↗

arXiv2024

Acquire Precise and Comparable Fundus Image Quality Score: FTHNet and FQS Dataset

The retinal fundus images are utilized extensively in the diagnosis, and their quality can directly affect the diagnosis results. However, due to the insufficient dataset and algorithm application, current fundus image quality assessment (FIQA) methods are not powerful enough to meet ophthalmologists` demands. In this paper, we address the limitations of datasets and algorithms in FIQA. First, we establish a new FIQA dataset, Fundus Quality Score(FQS), which includes 2246 fundus images with two labels: a continuous Mean Opinion Score varying from 0 to 100 and a three-level quality label. Then, we propose a FIQA Transformer-based Hypernetwork (FTHNet) to solve these tasks with regression results rather than classification results in conventional FIQA works. The FTHNet is optimized for the FIQA tasks with extensive experiments. Results on our FQS dataset show that the FTHNet can give quality scores for fundus images with PLCC of 0.9423 and SRCC of 0.9488, significantly outperforming other methods with fewer parameters and less computation complexity.We successfully build a dataset and model addressing the problems of current FIQA methods. Furthermore, the model deployment experiments demonstrate its potential in automatic medical image quality control. All experiments are carried out with 10-fold cross-validation to ensure the significance of the results.

eess.IV↗

arXiv2024

Versatile Cataract Fundus Image Restoration Model Utilizing Unpaired Cataract and High-quality Images

Cataract is one of the most common blinding eye diseases and can be treated by surgery. However, because cataract patients may also suffer from other blinding eye diseases, ophthalmologists must diagnose them before surgery. The cloudy lens of cataract patients forms a hazy degeneration in the fundus images, making it challenging to observe the patient's fundus vessels, which brings difficulties to the diagnosis process. To address this issue, this paper establishes a new cataract image restoration method named Catintell. It contains a cataract image synthesizing model, Catintell-Syn, and a restoration model, Catintell-Res. Catintell-Syn uses GAN architecture with fully unsupervised data to generate paired cataract-like images with realistic style and texture rather than the conventional Gaussian degradation algorithm. Meanwhile, Catintell-Res is an image restoration network that can improve the quality of real cataract fundus images using the knowledge learned from synthetic cataract images. Extensive experiments show that Catintell-Res outperforms other cataract image restoration methods in PSNR with 39.03 and SSIM with 0.9476. Furthermore, the universal restoration ability that Catintell-Res gained from unpaired cataract images can process cataract images from various datasets. We hope the models can help ophthalmologists identify other blinding eye diseases of cataract patients and inspire more medical image restoration methods in the future.

eess.IV↗

arXiv2023

OphGLM: Training an Ophthalmology Large Language-and-Vision Assistant based on Instructions and Dialogue

Large multimodal language models (LMMs) have achieved significant success in general domains. However, due to the significant differences between medical images and text and general web content, the performance of LMMs in medical scenarios is limited. In ophthalmology, clinical diagnosis relies on multiple modalities of medical images, but unfortunately, multimodal ophthalmic large language models have not been explored to date. In this paper, we study and construct an ophthalmic large multimodal model. Firstly, we use fundus images as an entry point to build a disease assessment and diagnosis pipeline to achieve common ophthalmic disease diagnosis and lesion segmentation. Then, we establish a new ophthalmic multimodal instruction-following and dialogue fine-tuning dataset based on disease-related knowledge data and publicly available real-world medical dialogue. We introduce visual ability into the large language model to complete the ophthalmic large language and vision assistant (OphGLM). Our experimental results demonstrate that the OphGLM model performs exceptionally well, and it has the potential to revolutionize clinical applications in ophthalmology. The dataset, code, and models will be made publicly available at https://github.com/ML-AILab/OphGLM.

cs.CV↗